{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from EMRM1D import EMRM_simulator\n",
    "import matplotlib.pyplot as plt\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1). Create simulator object"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "testModel = EMRM_simulator()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2). Set the layered model parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Layer thicknesses\n",
    "thicknesses = np.array([1.5,1.5,25,5,50,150])\n",
    "\n",
    "# dielectric constant values\n",
    "eps = np.array([3.15,4.3,3.15,4.3,3.15,4.0,8.5])\n",
    "\n",
    "# Loss tangent or conductivity values\n",
    "lossOrCond = np.array([0.002, 0.02, 0.002, 0.02, 0.002, 0.02,0.02])\n",
    "\n",
    "# 1 if using conductivity, 0 if using loss tangent\n",
    "useConductivity = 0\n",
    "\n",
    "# Spacecraft altitude in meters\n",
    "H = 300*1000\n",
    "\n",
    "\n",
    "testModel.setModel(H,thicknesses,eps,lossOrCond,useConductivity)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3). Set the radar pulse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load pulse from csv file. \n",
    "timeTest, pulseTest = testModel.loadPulse('Sharad_Ideal_sourcePulse.csv')\n",
    "\n",
    "# Set pulse\n",
    "testModel.setPulse(pulseTest,timeTest)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4). Set the radar matched filter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "tmfTest,fmfTest,mfTest = testModel.loadMatchFilter('Sharad_Ideal_matchedFilter.csv')\n",
    "\n",
    "testModel.setMatchFilter(mfTest,fmfTest,tmfTest)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5). Set the windowing parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "fmin = 15e6 #\n",
    "fmax = 25e6 #\n",
    "fnum = 6 # Use zero for no windowing, 6 is a Hann window\n",
    "\n",
    "testModel.setWindowParam(fnum,fmin,fmax)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 6). Run simulation and plot result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda2/lib/python2.7/site-packages/numpy/core/numeric.py:492: ComplexWarning: Casting complex values to real discards the imaginary part\n",
      "  return array(a, dtype, copy=False, order=order)\n"
     ]
    },
    {
     "data": {
      "image/png": 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Gy35Q72o9YkRHvzcU3thDSX4xGo0wGo0drq+rq/O285gwYQIAYNu2bbDb7W2GqNm5cyesVivkcjnGjBkThKrPDz/++CNuuukm2O12vPLKK3jwwQc7bc/PJTjGjh0LmUwGi8WCXbt24aKLLvJZb7fbsX37dgBnPoO+KFz2g3rXwYMHvfMZGRkhrIRChT2U1KmZM2d22mPmUVlZCUEQ8MILL3iXXXHFFYiNjYVer8fKlSvbbHvZsmUAgGnTpkGtVgd9X8LRzp078Ytf/AJmsxnPPvssnnnmmS5fw88lODQajbcH2fMzbO2TTz6BXq9HfHw8Jk+e3MvV+S9c9oN61+uvvw4AGDp0KNLT00NcDYVEsG/FQ+ENXdzL++WXXxYACCkpKR3eM/rnn3/urXLDyuHDh4WEhAQBgPDggw9267X8XIJj06ZNXd4D+9VXXw1hhf4Jl/2gwPnmm2+EZ555RigtLfVZ3tjYKDzyyCPevwXt3c6Vzg8MlNQjXQVKu90uTJ8+XQAgiMViYdSoUUJOTo73da+88kovVxw+rr76agGAIBKJhIkTJwoFBQXtTsuWLWvzWn4uwbN48WLvzzEnJ0cYNWqUIBaLBQDCjBkzBIfDEeoS/RIu+0GB8emnn3p/H9LT04Xx48cLY8aMEeRyufd76He/+12oy6QQYqCkHukqUAqCIDgcDuHNN98URo8eLURERAjR0dHClClThC+//LIXKw0/l19+uffn39nU0Zc8P5fg+eKLL4QpU6YI0dHRQkREhDB69GjhzTff7HchLFz2g3quoqJCWLhwoTBlyhQhKytLUKlUglKpFAYOHCjMnj2bRzRIEAlCqxPhiIiIiIi6iRflEBEREVGPMFASERERUY8wUBIRERFRjzBQEhEREVGPhF2gXLNmDaZOnYq4uDhERkYiPz8fb7/9Nlwu1zltr7i4GLfffjtSU1OhVCqRm5uL+fPno7GxMcCVExEFR6C/F4mIzhZWV3kvWbIEzz77LAAgJycHUVFROHDgAFwuF66//np8+umnEIv9z9AbN27EjBkzYDabkZiYiMzMTBw+fBgmkwk5OTnYsmULkpOTg7U7REQ9FujvRSKi9oTNt8jWrVuxYMECiMVifPjhhzh+/Dj27t2LXbt2ITk5GZ9//jkKCwv93p7BYMCvf/1rmM1mPProozh16hR27tyJiooKFBQUoLS0FPfcc08Q94iIqGcC/b1IRNSRsOmhnDFjBtasWYP7778f7777rs+6Dz/8ELfffjvi4+NRXV0NmUzW5faWLl2Kp556CsOGDcP+/fshkUi86yoqKpCbmwuHw4GdO3ciPz8/4PtDRNRTgf5eJCLqSFj0UOr1eqxfvx4A2u01vPnmm6HRaKDVarFx40a/trlq1SoAwF133eUTJgEgKysLU6dOBQCsXLmyJ6X3S+zR6Jv4ufQ9ofxMAv29yN8vag9/L8gjLALl7t27YbPZoFQq2+0tlMlkGD9+PACgqKioy+15eh4BoKCgoN02nuX+bC/c8Aukb+Ln0veE8jMJ9Pcif7+oPfy9II+wCJQlJSUA3D2HUqm03TY5OTk+bTtTVlYGu93u87qebI+IqLcF+nuRiKgzYREodTodACA2NrbDNp51nrb+bK+zbXZne0REvS3Q34tERJ1p/7+t/YzFYgEAyOXyDtsoFAoAgNls9nt7nW3Tn+0VFhb6HA6orq5GXFxcl+/f17lcLiQmJoa6DDoLP5fQEwQBZidgdbQsMIduvNqefi+e/f1VVVWFjIyMAFfZisEA6PXB235fJBa7J4nkzHxny0SiUFfchtFoDO7vBYVUXV0drFarX23DIlAqlUoAgM1m67CN5weiUqn83p5nm62fd2d78+bNw7x587zPMzIycPLkyS7fv69bt24dpk2bFuoy6Cz8XEKrqtGMhz7chd0VZ0Kk4/3fhqyenn4v9vr318svAy+9BPj5xyssuFzuyeHoui0AREYCiYlAQsKZqfXzs9fFxbmDKdE56s5/FsIiUPpz2Mafwz9nb8/zutTU1B5tj4jC17FaA/61uQz/23USFvuZO89EyCUIZX9boL8Xg27hQvfkcAAmE9DcfObx7Km95f62dTpDvafnzrMPZWX+tReJ3KHy7MA5YQJw663ugEoUIGERKPPy8gC4x4d0OBztnoBeWlrq07Yz2dnZkMlksNvtKC0tbTdQdmd7RBReBEHAjyX1eG/TCfxwtK7N+rykKPz1jnxMfi8ExXlqCPD3Yq+RSgGNxj0FmiAANlvPQ2lHy02mwNfcE4IAaLXu6ciRM8v/+U9g7Vpg5co+eRid+qewCJRjx46FTCaDxWLBrl27cNFFF/mst9vt2L59OwBgwoQJXW5PKpUiPz8fRUVF2Lx5c7tDB23evNnv7RFR/+dyCThQ1YT1xbX4al8Vjtc1t9vul/npWDxzBCLkof16DfT3YlgQiQCFwj0F43x2lwswm7sOqzodsH07cPiwO+j5cW5/wK1a5e6t7WAEAKLuCovfJI1Gg6lTp2Lt2rVYtmxZmy/OTz75BHq9HvHx8Zg8ebJf2/zlL3+JoqIiLF++HE888USbO+V4Bgy+8cYbA7YfRNS3WOxObDlej28P1eK7wzWo0bd/fp9cKsasMemYc2k2hqYEoWftHATje5G6IBa7DyN7znVszxdfAI8/DjQ19W5tZxs82F0vUYCEzW/TwoULIRKJ8M9//hMfffSRd/nevXu9J5Y/9dRTPlc8rly5EtnZ2bj00kvbbO+BBx5AQkICiouLMW/ePO+4lFqtFrfddhscDgeuueYajBs3Lsh7RkS9RRAElNYZsWJbBe77zw6M/f23uHv5Dny0raLdMJmoVuCJqwZj6zNT8OpNo/pMmPQ4l+9FCiJBAObODV2YFIvdQfe664BPPmGgpIAKm3t5A8DLL7+MRYsWAXAP2BsVFYUDBw7A5XJhxowZ+Oyzz3x6GpcvX445c+ZgwIABKGvnJOcNGzbguuuug8ViQWJiIrKyslBcXAyTyYTs7Gxs3boVKSkpftfHq7wpmPi5dJ/Z5sS+k43YWaHDrnIddpbroDPZO32NSibBZYMTcO3IVFwzIhVyacd/lPvCv/nufi92pC/sS79nNgMREYHbnkbT8RXeZz9PSABiYxkiqVu68+8+LA55eyxcuBCjR4/GG2+8gZ07d+L06dMYOXIk5syZg4cfftivL83WrrzySuzYsQOLFy/Gd999h/379yM9PR2zZs3CokWL+saVkUTkt+omM3a2BMdd5TocrNLD4er6/9QpGiWmXpCEK4cl45KceChl/WcolkB/L1KQyOWdDwHUXkBkzzL1IWHVQ9nXhcv/8NkT1jfxcznDYneipMaIw6f1OHLagCM1Bhw+bUCdwb8xDkUiYHiaBlOHJWPqsGQMT9NAdA5Xw4bLv3kgvPYlZNrroVy8GHj0USAqildcU59z3vZQEtH5xeUSUNFgwuHThpbgqMfhagPKtM3wo+PRS62QYkxWDMYNiMW4AbEYkxkDtVIWvMKJPOLiALU61FUQ9RgDJRH1eXqLHRVaEyoaTCjXmnCi3ogjpw04WmOE2d79gaqz4yOQ3xIexw2IRV6SGhIxe4eIiM4VAyURhZzLJeC03oKKBhMqtCaUNzSjosGMCm0zyhtMaOziQpmOiEXAwIRIDE3RYEiKGkNT1BibFYtEtSLAe0BEdH5joCSioBMEAXqLAzV6i7en0d3b2IyKBhMqdWbYHK6uN9SJZI0CQ1I0GJqixpBkNYakqDEoKapfXUBDRNRfMVAS0TlzugQ0NNtQa7Cg1mBFnd7qna9tNV9nsMLaw8DokaRWICsuAnnJ7h5HT89jTASveCUiChUGSiJqw+Zwoc5oRa2+JRwarKhrNV9rsKBWb4W22QZnd65+8YNMIkJmbASy4iOQFeeeBsRHeudVcvY4EhH1NQyURGHI5nDBaHXAaHHAYLXDaHG4n1sdMHjmLa2f22GwOKA1unsbuxrcu6c0Sqk7JMZHYEBLUMyKdwfHFI2SF8gQEfUzDJREfYQgCLDYXb4B0OKAoVX4ax0AfQOhA7U6BxZs+xYGq6PH5yOeK7lUjCS1AklqBRLVCiSple7nGvd8olqBzNgIREdwSB4ionDCQEkUIC6XAL3FjnqjDVqjFQ3NNhh8AqG9kx5C99Tzw8e2gOzL2dQKKRI1nqCo9IZGT1BMagmPGpX0nAYAJyKi/o2BkqgTJpv7MHC90Qqt0QZts7UlMLrnveuabdA12/y6jV8oiUVAlEIKtVKGKIUUUUrpmUe5FHFRcm84TNKc6WmMkPOrgoiIOsa/EnResTtd0DXb3KHwrECobQmN9a3mz2XQ7GCQSURnQmBLAFSfFQhPV5Rh7IihiGppp1a2bauSSdiDSEREAcdASWFPEAS8/FUxVu0+hYbm4BwS7ohKJmkb/toEQtmZ563atQ6ECmnXVzavW1eBaQUDe2GviIiIfDFQUtg7WKXHPzed6LX3i1bJkBmnQkZMBKJVMkQoJIiQSxAhl7Y8tp53P0YqJFDJpYiUS6CSSyCXiNmTSERE/QYDJYW9ZI0SaqUUBoujV96vyWxH0yk7DpzSn/M2pGIRVHIJIj3BUyFBhEzaTjh192QmqRU41eBCbq0RKdFKRCn4T5uIiHoP/+pQ2EtUK7Dm0UnYeKQWBosDJpsDJpsTJqsTJrsTJmvLc8/ylvlmmzNkw+84XIL7CvFuhuA39/4AwH1Vdkq00j1plEiNViIlWoWUaAVSNCqkRisREyFjLygREQUEAyWdFzLjIjD7kuxuv87hdMFkd8Jsc6LZ6hs42523Os4KqR23DSaD1QFDrREltcYO2yikYp/AmRytRKrGEzzdy5LUCoZOIiLqEgMlUSekEjE0EjE0ysAOxO1yCbA4nGi2toRVm28vqWeZO8g6YbI73D2qPqHU/dhosqPWYIHd2b0hi6wOF8q1JpRrTR22iZRLkJesxpBkNfKSozAkRY3ByWoGTSIi8sFASRQCYrGo5TzIwPwTdLkE/O+rdRiWfwmqmyw43WTGab2lZd49VTdZuj0MUrPNiT2VjdhT2eizPFolw+DkKAxOVmNIihp5Se7HuEh5QPaHiIj6FwZKojAgFougkYswIj0aI9Kj220jCAL0FkdLuDSjplXgrG6yeJ83mbu+j3eT2Y7tZTpsL9P5LE+IkmNwshrD0zS4MDsOFw6IRXyUIiD7SEREfRcDJdF5QiQSIVolQ7RKhiEp6g7bmW1OnNZbcFJnQkmNESW1Bhw5bUBJjREGa+cXCdUbbag3arHluBb/+Mk9VFNOQiTGDYjF+Ow4jMuORU5CJA+XExGFGQZKIvKhkkswMCESAxMiMSkv0btcEARUN1lwpMaAkhoDjpx2h82jNQZY7B1fDV9a34zS+mZ8svMkACA+Uo78AbEYn+0OmaMyYiARM2ASEfVnDJRE5BeRSIS0GBXSYlS4YkiSd7nLJeCkzowjNe5wWVytx+6KRpxqNLe7HW2zDd8eqsG3h2oAAHGRckwZmoSpw5IxKS8BkRxDk4io3+E3NxH1iFgsQlZ8BLLiI3DVBcne5VWNZuwo12FHWQN2lOlw+LQernYuRG9otmHlzpNYufMk5FIxCnLjMfWCZFw5NBkp0cpe3BMiIjpXDJREFBRpMSpcH6PC9aPTAAAGix27KxrdAbNch90VjW2uOrc5XNh4pA4bj9RhIQ5gVEY0br4wE7PGpvPuP0REfRi/oYmoV6iVMlw2OBGXDXafl2l3urC9rAHrD9Xi2+LTqGxoe4h838km7DvZhCVrinHD2HTcMWEALkjT9HbpRETUBQZKIgoJmUSMibkJmJibgOeuG4aSWiO+PVSD9cU12FPZCKHV4fFmmxMfFlXgw6IK5GfF4I6LB+D60WmQSsSh2wEiIvJioCSikBOJRBic7L4Lz0NXDEKtwYL1h2rx8Y5K7D1rUPVdFY3YVdGIf/50AotnjUB+VmyIqiYiIg/+956I+pwktRK3TcjCZw8V4IuHL8Ut4zOhkkl82hyq1uOXf9mCZ1ftg67ZFqJKiYgIYKAkoj5uZEY0ltw4CkULr8TvbxiO7PgIn/UfbavElNe/x46yhhBVSEREDJRE1C9olDLMviQb6x6/DE9OGwKl7Mzd6AgEAAAgAElEQVTXl85kx33/2YHKBlMIKyQiOn8xUBJRv6KQSvDQFYPw7eOXY+qwMwOs60x23PvvHTB2cXtIIiIKPAZKIuqXMuMi8I/ZF+KmcRneZUdqDPj3lrLQFUVEdJ5ioCSifkskEuHlWSMwNEXtXbb/ZFMIKyIiOj8xUBJRv6aQSpAeo/I+l0v5tUZE1Nv4zUtE/dq6g6ex8Uit97lGxeF1iYh6G795iahfEgQB6w7W4NEVu+FquauOWAT8Mj+j8xcSEVHAMVASUb+z72Qj/rCmGD+X+o49+fKskbxzDhFRCDBQElG/caK+GW98exSf761qs+6Jqwbj1ouyQlAVERExUBJRn6ZrtuHL/dVYvfsUdpbr2qyPj5Rj/rQhuGV8ZgiqIyIigIGSiPogi92J7w7X4tPdp/D9kVrYnUKbNiqZBPdNGoj7L89FlIJfZUREocRvYSIKOUEQUK41YfPxemw+Vo+fjtbD0MEdb6RiEW4al4HHrxqMZI2ylyslIqL2MFASUUjUG63YclyLzSX12HSsHqcazZ22z8+Kwaz8DMwYmYq4SHkvVUkUZC+/DKxYASQmAgkJ7qn1fOvnERGhrpaoQwyURBR0giCgusmCfScbsbNch03HtCiu1nf5uuz4CMwam4GZY9MwID6yFyol6mWnTrknf6hUbQNnR+EzIQGIjwek/DNPvSPsftPWrFmDwsJC7Nq1C1arFUOGDMGcOXPw0EMPQSz2fxz35cuXY86cOZ22Wbt2LaZPn97TkonCTkOzDXtPNmJfZRP2nWzE3pNNqDdau3ydWASMzoxBQW4CrhyWhDGZMRCJRL1QMVEvkMuBqCjAaDy315vNQGWle/JXbGzbsDl2LHDXXe5aiAIkrALlkiVL8OyzzwIAcnJyEBUVhb179+LRRx/F+vXr8emnn3YrVAJAUlIS8vLy2l0XG8vx7ogMFjsOnNJj38lG7DvZhL0nG3FS1/nh69ZyEyNx6aAEFAxKwISceESrZEGsliiEJBJg7lxg6dLee0+dzj2VlPguX7sW+Oqr3quDwl7YBMqtW7diwYIFEIvFeP/993HrrbcCAPbu3Ytp06bh888/R2FhIebPn9+t7V5zzTVYvnx5ECom6l9MNgeO1RpxtMaIkhoDjtYYcLTG2OW5j2dLj1HhooFxKBiUgIJB8UiNVnX9IqJwsWQJMH06UFQE1Nf7TnV17keDIfh1rFnjDprsGKEACZtAuXjxYgiCgPvuu88bJgFg9OjRKCwsxO23344lS5bgscceg0zGHhCijljsThyvM3oDY0nLY6XOBKHt6D2dio+UY1RGNEZlxGB0ZjRGpscgUa0ITuFE/YFYDEyZ4p46YrUCWu2ZgHl24Dz7eV0dYLd3vxajkYGSAiYsAqVer8f69esBAPfcc0+b9TfffDPmzp0LrVaLjRs34uqrr+7tEon6FJdLwGm9BWXaZpRrTSjTNuNEXTNKao0o1zZ7743dHVEKKUakazA6IwajM2MwKiMa6TEqngNJ1F0KBZCW5p78IQjucNhZAN2/390rShQkYREod+/eDZvNBqVSifz8/DbrZTIZxo8fjw0bNqCoqKhbgXLv3r247bbbcPr0aWg0GowdOxZ33HEHcnNzA7kLRAHncLpQ1egJjc0o05q8jxUNJtgcrnParlgEZCdEYnCSGoOTo5CXrMawVDVyEqIgFjM8EvU6kQhQq91TTk77bT75BPjVr3q3LjqvhEWgLGk52TgrKwvSDoZIyMnJwYYNG7xt/bVnzx7s2bPH+/yzzz7DSy+9hBdffBELFy4896KJAqDZ6kBVoxmVOhM2VLqw9fOD3l7HygYTHOfS1dhCJAIGxEUgL9kdHAcnq5GXpEZOYiSUMkkA94KIiPq7sAiUOp37/r6dXXXtWedp25WYmBg88sgjuOWWWzBo0CBER0ejuLgYhYWF+O9//4tFixYhOjoaDz/8cIfbKCwsRGFhofd5Y2Mj1q1b59f792UWiyUs9qOvcwkCDDagwQI0WAVoLUCDRUCDteXRAjSffTOZY2Xdfh+ZGEhUAYkqEVIigLRIEdIi3fNyiQ2A1j3VAJU1QDcGLCEiovNEWARKi8UCAJDLO757hkLhvhDAbPbvitSZM2di5syZPsvGjBmD//znP4iPj8ebb76JRYsW4c4774RarW53G/PmzcO8efO8zzMyMjBt2jS/3r8vW7duXVjsR6hZ7E5UN1lwSmdGVaMZp1omz3x1owU257kdlj5bhFyCAfGRyI6P8H1MiECyWslD1URE1CNhESiVSvf9fG02W4dtrFb3oMoqVc+HKHnxxRfx17/+FU1NTfjuu+9www039HibFD4EQYDOZEeN3oLTegtq9RbU6K2oaXmsNVhQ1Wjxa6Dv7lBJgbyU6HaDY0KUnBfHEBFR0IRFoPTncLY/h8X9pdFoMHz4cOzatQvHjh3r8faofxAEAXqLwzcgGiyo9YZF9/I6gzVgPYutJUQpkB6rQnqMEukxKqS1TOktU9FP32H69EsD/r5E1Md5rvLubGih/ftDXSWFubAIlJ472VRUVMDhcLR7YU5paalP257yjGXpcJx9Ehv1N06XAJ3JhnqjFfUG92Odwdqqh9GKGoM7MFrsgQ+KACCXiJEWo/QGxLQYVUt4dM+nRiu7vBCGPZBEYcJm82/8ydbznRyhI+oNYREox44dC5lMBovFgl27duGiiy7yWW+327F9+3YAwIQJE3r8fk6nE0eOHAHgPi+S+h6H04UGk80bED1TncGKeqPNZ76h2XpO4y76SyEVI1mjRLJGgSSNEslqJVKiFUiPiUB6rAppMUokRCp4HiPR+cDlAn74oe2dclqHRL2+d2rhvbwpgMIiUGo0GkydOhVr167FsmXL2gTKTz75BHq9HvHx8Zg8eXKP32/ZsmVobGyERCIJyPbIP3anCw3NtpYgeHYwtPr0MDaYbN2+q0t3ScUiJGuUSNIokKxuFRhbwmNyS3jUqKTsPSQitwULgFdfDXUVwDXX8C45FFBhESgBYOHChfj666/xz3/+E5MnT/a5l7fnSuunnnrK50rwlStXYv78+cjIyMCmTZu8y/V6PX7729/i8ccf9wmnTqcT7733Hh577DEA7rvypKen98buhS2bwwVtc6tDzT4B0Yb6VmFRZzqHW4udA4lYhLhIOVLO6lX0hMSklse4CDl7FYnIf04n8M47vfueMTFAQoJ7Skx0P44dC8yZ07t1UNgLm0BZUFCAl156CYsWLcJtt92GRYsWISoqCgcOHIDL5cKMGTPwxBNP+LzGaDSivLy8zbZcLhdWrFiBFStWICYmBgMHDoRUKkVJSQkaGxsBANdccw3eeuutXtm3/sbqcLYJg57Dy3VGa6vlNjSZeyckSsUixEfJkRCl8E6JagUSouQtj55JjlgGRSIKBpvNffHMuVIqz4RCz+PZYbH187g4oOV8f6JgC5tACbh7KUePHo033ngDO3fuxOnTpzFy5EjMmTMHDz/8MCQS/+7uERkZiddeew1btmzBgQMHcPz4cZjNZsTHx2PGjBmYPXs2br755vP6MGat3oKtpVrsrmhErcHi08NosPTOhUoyicgnCCZEKZCgViCx5TEhSu6ej1IgWiVjSCSivictDRg0yL+AGBER6mqJOiQShGCfaUYeGRkZOHnyZKjLOCd1Bit+LtVia6kWG/ZXoMYUnPeRS8UtIbBVb6Ja3qpHsaV3MUrBcxPPwgHn+57+/G/+bOG0LyFjNrcNhX/5CzB3bmjqIepCd/7dh1UPJQVOk8mOTcfqvSHyWO25H6ZRysRnHWpu/9BzgloBtYIhkYiIqL9hoKQ2dlXocOd72/w6dJ2TGIlBiVEth5gVSGx16NkTFiPlEoZEIiKiMMZASW18feB0h2EyRaPE4Cgrbrp8DC7OiUOSWtnL1REREVFfIw51AdT3TB+Rgkh5+xcwaZutMDmApJaLX4iIiIgYKKmN/KxYfDd/Mh6fOhgpGt8eSLtTwI5aAbf8/Wdc/caPWL75RK8N/UNERER9EwMltStZo8RjU/Ow6ekr8O5vxuGywYlt2pTUGvHCF4dw8R824OmV+3CwqikElRIREVGo8RxK6pRUIsa04SmYNjwF5dpmfFhUgfe3lqK5Vaek2e7Exzsq8X87K3H/pBzMu3owFFL/xvwkIiKi/o89lOS3AfGRePbaYXj1Egne+PVojBvgex9YQQDe/bEUs97ZgpIaQ4iqJCIiot7GQEndJpOIMGtsBv43dyLWPjYJd1ycBbn0zK/SoWo9rnt7Ez7bcyqEVRIREVFvYaCkHhmWqsHimSPx5SOXYmiK2rvc6nBh/id7saOsIYTVERERUW9goKSAGJysxmcPF+D+y3K8y+xOAXM/2IXTTZYQVkZERETBxkBJAaOQSrDg2mF4avoQ77I6gxWPrtgNl4u3jCciIgpXDJQUcHMvz8WMkane59tONODDbRUhrIiIiIiCiYGSAk4kEuG1m0YhI1blXbZk7WFUNZpDWBUREREFCwMlBUWkQopXfjnS+9xodeDlNcUhrIiIiIiChYGSgmZSXiJuzM/wPv9qXzW2HKsPYUVEREQUDAyUFFTPXDMUasWZGzK9sf5oCKshIiKiYGCgpKBKVCvwwORc7/M9lY2wOVwhrIiIiIgCjYGSgu6S3HjvvN0p4HidMYTVEBERUaAxUFLQndL5Xt1tsjlDVAkREREFAwMlBZXJ5sAfWl3dnaRW4IJUTQgrIiIiokBjoKSg+nh7Japb3XpxwbXDoJJLQlgRUf9y1113QSQSdTpZLO3f3rS4uBi33347UlNToVQqkZubi/nz56OxsbGX94KIwp206yZE5271nirv/OjMGNwwJi2E1RD1X3l5eUhKSmp3nVjctm9g48aNmDFjBsxmMxITEzF8+HAcPnwYr7/+Oj799FNs2bIFycnJwS6biM4TDJQUNJUNJuytPNMTcrzWiHv/vQMXDYzDRQPjMCI9GjIJO8mJ/LFgwQLcddddfrU1GAz49a9/DbPZjEcffRR//OMfIZPJoNVqccMNN2Dz5s2455578OWXXwa3aCI6bzBQUtDUG60+z41WBzYcrsWGw7UAgAi5BOMGxOKi7DhMyInHqIxoKGU8HE7UU3/7299QV1eHYcOGobCwEBKJ+99VfHw8PvzwQ+Tm5uKrr77Crl27kJ+fH+JqiSgcMFBS0IzJjMHtE7LwyY6TsDnbjj1psjnxU0k9fipx3z1HKhYhJVqJtGgV0mKUSI1RIS1aidRoFdJi3MuiVTKIRKLe3hWifmXVqlUA3OdfesKkR1ZWFqZOnYqvv/4aK1euZKAkooBgoKSgEYlEeHnWSDx33QXYU9mIbScaUHRCi53lOljsbQOmwyXgpM6Mk2cNM9SaSiZBWowSaTEqpLaEzfQYFVJjPMFTiQg5f60p/KxcuRKrV6+GXq9HUlISCgoKMHv2bERHR/u0czgc2LlzJwCgoKCg3W0VFBTg66+/RlFRUdDrJqLzA//yUtApZRJcnBOPi3PiAeTB5nDhQFUTtp1owLYTDdh+ogEGq8OvbZntThyva8bxuuYO28REyNzhMroleMZ4ej3dITQlWslzN6nf+eqrr3yef/zxx/jd736HDz/8ENOnT/cuLysrg91uBwDk5OS0uy3P8pKSkiBVS0TnGwZK6nVyqRj5WbHIz4rFA5fnwukSUFytR3G1HtVNFlQ3mVHVaEFVoxnVTRYY/QybHo0mOxpNdhRX69tdLxIBiVEK72F07yH16JbD7DFKJEQqIBbz0DqFXm5uLv7whz9gxowZGDhwIEQiEbZu3YrnnnsORUVFmDlzJjZt2oQLL7wQAKDT6byvjY2NbXebnuWt256tsLAQhYWF3udGI+9wRUQdY6CkkJOIRRiRHo0R6dHtrtdb7KhuCZhVTWb3fJPZGzirGy3tnqPZEUEAag1W1Bqs2FPZfhuZxH0+p/eQekvYTG8JoJlxEYhS8J8PBd9zzz3XZtlVV12Fyy+/HJMmTcK2bdvw9NNPY8OGDQDgMyalXC5vd5sKhQIAYDZ3fHrJvHnzMG/ePO/zjIyMc6qfiM4P/ItIfZ5GKYMmRYYhKep217tcArTNNm/PZnVL2KxqsqC6JXTW6C1wCf6/p90poLLBjMqGjv/gxkfKkRUfgQFxEciKj0RWXAQGtDxPVCt48RAFlVwux0svvYRp06bh+++/h06nQ2xsLJRKpbeNzWbzee5htbpHYFCpVL1WLxGFNwZK6vfEYhES1QokqhUY1UEnisPpQo3BiuqWoFnVaPadb7KgodnWrffVNtugbbZhd0Xbu46oZBJkxUUgKz7CGzTdj5FIj1FBLuU5nNRzl1xyCQDA5XKhtLQU48aN8znMrdPpkJqa2uZ1nkPdHR0SJyLqLgZKOi9IJWKkx7gPX3fEYneiuiVgeg+nN5lxqvFMT6e/53Oa7U4cqTHgSI2hzTqxCEiLUWFAfATyktQYnKzGkJQo5CWroVHKznkf6fwjk535fXE43L+b2dnZkMlksNvtKC0tbTdQlpaWAnDffYeIKBAYKIlaKGUSDEyIxMCEyA7b6C12nNKZUa41oaKhGRUNppZ5E07qzHD6cVzdJcA7PNLmY1qfdanRSuQlqzEk2R0wByerkZcUhUier0ntOHjwoHfec46jVCpFfn4+ioqKsHnz5naHDtq8eTMAYMKECb1TKBGFPf6VIuoGjVIGTaoMw1I1bdY5nC5UNVpQ3tDsDZnl2jPzJpuzy+27e0Ut+PFonc/yjFgVhiSr3WEzJQoj02OQkxDJK9HPc6+//joAYOjQoUhPT/cu/+Uvf4mioiIsX74cTzzxhM/g5hUVFVi/fj0A4MYbb+zdgokobDFQEgWIVCJ2nzMZH4FJZx1JFAT3hUOens1yrQmldc04WmNAaV1zl1epe3o0PbetBAC1UooxmTEYmxWLsZkxMNq7cdUR9QvffvstvvvuO9x///0YOHCgd3lTUxOee+45fPTRRwCA559/3ud1DzzwAJYuXYri4mLMmzfP517et912GxwOB6655hqMGzeuV/eHiMIXAyVRLxCJREiIUiAhSoFxA3wvhHA4XSjTmlDScs5lSY0RR2oMOFHf3OkhdIPF4XPrSgD48+HvMTYzBmOyYjA2MxZDU9UcxL0fa25uxpIlS7BkyRKkp6cjLS0Ndrsdhw4dgs1mg0gkwvPPP49bb73V53UajQYrVqzAddddhz/96U/46KOPkJWVheLiYphMJmRnZ+O9994L0V4RUThioCQKMalEjEFJURiUFIVrRp65gMLqcOJEfTOO1hjdYfO0ASW1RpRpmyF0kDNP1DfjRH0zVu0+BQBQSMUYnx2HyUMSMWVoEnISo3pjlyhAxo0bh4ULF2Lr1q04duwYDhw4AEEQkJ6ejkmTJuHBBx/s8DzIK6+8Ejt27MDixYvx3XffYf/+/UhPT8esWbOwaNEiXuFNRAHFQEnURymkEgxN0WBoiu/5mkarA/tONmJ3hXvaU6lDvbH9IY+sDhc2HavHpmP1WPxVMbLjIzB5SBKuGJqECQPjoJRJ2n0d9Q2ZmZlYvHjxOb9++PDh3sPiRETBxEBJ1M9EKaSYmJuAibkJANznZ57UmfHvL3+AMzYLeyobcfCUvt3zMsu0JizfUoblW8qgkklQMCgeV12QjOtGpfFKciIiOmf8C0LUz4lEImTGRWB8shjTpg0H4D5cfqhKj20nGvD9kTpsL2uA46zzMc12J9YX12J9cS0Wf1WMm8dl4jeXDOh02CQiIqL2MFAShSGFVOK++jsrFr+9PBd6ix2bS+qx8UgtNh6pQ53B6tPeYHHgvc0n8N7mE7h8cCLunDgAlw9OgoTDEhERkR/C5vLPEydO4B//+Afuu+8+jB49GlKpFCKRqEfnHwHA1q1bccMNNyAxMREqlQoXXHABXnrpJVgslgBVThR8GqUM14xMxWs3jUbRs1fiy0cuxbyrBmN4WtvxNH84Woe7l+/AjD/9hCOn297ph4iI6Gxh00P51ltv4a233groNj/44APceeedcDqdSE9PR2ZmJg4cOIDnn38eX3zxBb7//ntEREQE9D2Jgk0sFmFEejRGpEfjkSmDsKeyEf/ZWo4v91XB7jxzWPzwaQOu//MmvHD9cNwyPhMiEXsriYiofWHTQ5mQkIDrrrsOv//977F27doe3wGirKwM99xzD5xOJ1577TVUVlZi165dKCkpwZAhQ7B9+3Y89dRTAaqeKDREIhHGZsXijV+PwZZnrsQTVw1GikbpXW91uPDsqv14dMUe2BydD75ORETnr7AJlIsWLcIXX3yB5557DtOnT0dUVM/G21u6dCmsViuuvvpqPPnkk97emQEDBngHBP773/+OmpqaHtdO1BckqhV45Mo8/PjUFfjtZTk+677YW4W//XA8RJUREVFfFzaBMpAEQcCnn34KALjnnnvarJ84cSKGDh0Ku92Ozz77rLfLIwoquVSMZ68dhn/NGY+4SLl3+cfbK+Hq5M49RER0/mKgbEdFRQWqq6sBAAUFBe228SwvKirqtbqIetMVQ5Lw6o2jvM9PNZqxo1wXwoqIiKivYqBsR0lJCQBAoVAgLS2t3TY5OTk+bYnCkeqsO+mU1PKqbyIiaouBsh06nbsXJiYmpsMrWz33wfW0JQo3J3UmLFy93/tcJZPgiiFJIayIiIj6qrAZNiiQPGNMyuXyDtsoFAoAgNls7rBNYWEhCgsLvc8bGxuxbt26AFUZOhaLJSz2I9wE6nOxOQWsqxCwrsIFe6sLu6/PFrC/6Efs7/ilRER0nmKgbIdS6R42xWazddjGanXfaUSlUnXYZt68eZg3b573eUZGBqZNmxagKkNn3bp1YbEf4aann4vd6cI3B2vw6tpinNT5/kfpouw4/GHOxbxzDhERtYuBsh2ew9mNjY0QBKHdw96eQ92etkT9kSAI2F3ZiNW7T+HLfdVoaPb9T5RIBPz6wkwsmDGMYZKIiDrEQNmOvLw8AO5eyKqqKqSnp7dpU1pa6tOWqD8prTNi9Z4qfLbnFMq1pnbbjM6Mwe+vH47RmTG9XB0REfU3DJTtyMrKQkpKCk6fPo3NmzfjV7/6VZs2mzdvBgBMmDCht8sj6rZ6oxXbTjTg51Itth7XoqTW2GHbtGgl/t9Vg3FTfgbE7JUkIiI/MFC2QyQSYdasWfjrX/+KZcuWtQmUW7ZsweHDhyGTyXD99deHqEqijtUbrSgqdQfIn0s7D5AAoFZKMWNkKm4Yk44JA+MYJImIqFvO60D55ptv4s0338TFF1+MFStW+Kx78sknsWzZMnzzzTdYunQp5s+fD5FIhPLyctx9990AgHvvvRcpKSmhKJ3Iy2J3oqTGiE1VLvy0ej+KShu6DJAAIJeIccXQRMwam47JQ5KgPGvMSSIiIn+FTaDcvHkzbrjhBu9zo9H9B/WVV17Bm2++6V2+e/duZGZmAnBfdFNeXo7s7Ow22xs4cCD+8Y9/YM6cOXjqqafw1ltvISkpCQcOHIDdbse4ceOwdOnS4O4U0Vm0RiuKqw04VN2EQ1V6FFcbcKzOCKf3logVnb4+Oz4CF+fE4+KceFwxJAnREbLgF01ERGEvbAKl3W6HVqtts9xkMsFkOnPRgdPp9Hubs2fPxqBBg/DKK69gy5YtOHToEHJycnDrrbfi6aef9g4vRBRodqcLFQ0mHG4VHg9V61Gjt3ZrO60D5IScOKRGdzzMFRER0bkKm0A5efJkCILQdcNWXnjhBbzwwgudtpk4cSK++OKLHlRG1D6H04WTOjNOaJtRXt+MMq0JJ+qbUaZtxkmduVWvo3/EImBwshqjM2JwSS4DJBER9Z6wCZREfZHD6UJVowUntM0oq2/GifpmlGvd4bGywQRHN0Ojh1opxQWpGgxL1eCCNA0uSNWgbN9WXHfNZQHeAyIioq4xUBL1gMXuRHWTBdWNZlS1fmwyo6LBHRrtznMLjR4Zsao24TEjVtVmwP1TB3hlNhERhQYDJVEHbA4XavQWVDWaUd1kQVWTGdWN7rBY3WRBdZOlzZ1lzlWUQorshAhkx0diYEIksuMjkZ0QiUGJUbxwhoiI+jwGSjrvCIIAvdmBOqMV9UYr6gzWMyGxJTBWNVlQb7Sim6fldipCLjkTGFuHx4RIxEfK273FJxERUX/AQElhwekSoDPZUG+0ot7Q8mi0os5ohdZ45nm9wQZts7XHh6HbIxWLkKxRIi1GidRoFdJjVRjY0tOYnRCBxCgFQyMREYUlBkrqs2wOFxqabd5gWG+wor4lHGqNZ+brjVY0NNtwjte3+EUsApLUSqTGKJEWrUJqtBIp0Uqkxbjn02JUSIhSQMI7zBAR0XmIgZKCThAEmO1O6Ex26JptaDTZ0Wi2QWeyo7G55dFkg85kQ6PZjkaTHQ3NNjSZ7b1WY0KUoqVn0d27mBajREq0CmnRSqTGqJCkVkAmEfdaPURERP0JAyV1i8Ppgt4m4Fit4UxANHsCYctjsx06kzsQ6lqW2xyuXq1TLALiIhVIiJIjUa1AQpQC8ZFyJLTMJ0TJkRClQKJagbhIOcMiERFRDzBQnqcEQYDR6kCj6UzoazTZvM/PXu5ZZrA43BvY/GOv1yyTiFrCoDsQxreaT/QGRQXio+SIjZDz8DMREVEvYaAMAzaHy9tD6A5+niB4JgyeCYZnlp/roNqBolZKERshR2yEDDGtHmMiZIiNkPsEx8QoBTQqKS9qISIi6oMYKPuYZqsDdQarTy9hY6sw2N7yZpv/9ycPBrlUjNiWEBjjffQERE9Y9A2O0SoZpDzMTEREFBYYKEPI6RJQUmvA7opG7K7QYVdFI47VGkNWj0gExKh8ewk9j7ERMkS3PB47uBdXXT6xZbkcKrkkZDUTERFR6DFQ9iKXAHx3uAa7yhuxu1KHvZVNMFodQXmvCLkEsRFyRKtkiI080zPYWe+hRimD2I/zDtdV7601SHUAACAASURBVMfwtOig1E1ERET9DwNlL6rVW3D38h3deo1ELDrrUHFLMIw8q/dQJUdspMwbIpUy9hoSERFR72Cg7EMuyo7DL/PTMSxV4+5JjJRBreCFKERERNS3MVD2IdvKGrC9vAGZsRHITYxEbmIUcpOikNNyv+fEKIVfh6SJiIiIehMDZS/yp6NREICKBhMqGkzYeKTOZ51CKkZWXIR7inc/Dmh5zIiN4GFuIiIiCgkGyl6UrFHi52evxPE6I0rrjDhe14zjdUYcrzWiqsnS5eutDhdKao0o6eBK8BSN8kzQbBU6s+IiEBcp56FzIiIiCgoGyl6WEq1ESrQSBYMSfJY3Wx04UX8mYHrCZml9s9+3LTytt+C03oJtJxrarItSSJGdEIERadEYmRGNUekxGJKihlzKsSCJiIioZxgo+4hIhRQj0qMxIt13OB6nS0BVo9l7GLxca0JlgwnlDc0o15rO3AqxC0arAwdO6XHglB4rtlcCAOQSMYamqjEyPRqjMqIxMj0GeclRvK81ERERdQsDZR8nEYuQGReBzLgIFLSzvtFkQ7nW5A2cFVp32KxsMKOqyQyhk7sr2pwu7DvZhH0nm/BBkXuZQirGBWkajEqPRv6AWEwdloxIBX9NiIiIqGNMCv1cTMtA5aMzY9qsszqcOKUzo7wlaB6pMWD/ySYcPq2H3dl+0rQ6XC137mnEv7eWI1IuwXWj0vCr8RnIz4rleZhERETUBgNlGFNIJchJjEJOYpTPcqvDiSOnDdh3sgn7TzZh78lGlNQa4XS1DZnNNic+3lGJj3dUIjcxEr+6MBNxtk66PYmIiOi8w0B5HlJIJRiVEYNRGWd6Nc02Jw5V67H/ZCP2nWrCnopGlNY3+7zueF0zXll7GGIRUB1RgkevzOvt0omIiKgPYqAkAIBKLsG4AbEYNyAWACAIAg5W6fHx9kqs3nPK5+IflwAUfnsUEXIJ7p2UE6qSiYiIqI9goKR2iUQi71XnC2cMw7qDp/F/Oyqx+ZjW22bxV8VIVCtww5j0EFZKREREocbxYahLSpkEN4xJxwf3Xoz375kASavrcp5dtR9NZnvoiiMiIqKQY6Ckbrk0LwE3DDzza2OyOWG2OUNYEREREYUaAyV1m9F+5irvJLUCyRpFCKshIiKiUOM5lNQt+0424rtTZwLlhdkcm5KI6JytXQtIJEB6OpCR4X6Mjwf4vUr9DAMl+a3eaMUD/92J1rcWv2PCgNAVRETU333xhXtqTaFwB8vWIdPz6JlPSQFkstDUTNQOBkryi9nmxL3/3oGqJot32WNX5mHioIQQVkVE1I9IJO7J2cV55/+fvfuOi+LM/wD+GXqTKl0UUBNUbFFBMZaYaE6jMcYSTcHGJdFUuVju4imW00RznCZ3mrOdP3NJLIdYg8TEEgVjBwVBERAQEGTpbVl25/cH7upKW9hFYP28X699sTszz8x3jnPzYeaZ55FKgZSUmld9BAFwdq47cPr5AT4+uq2dqBEMlNSoarkCH/14BTEZhaplL/o44RMObE5EpDkTE2D0aODYMe33JYrAvXs1r0uXaq9fvhwICdH+OEQa4kM51KjVRxPwS0Ku6rOHFbBhej8YGLCPDxFRk3z/PbB4MTB8ONC1a83t7ZbwxRdAZWXj2xHpCK9QUoMOXM3Ezug7qs/utub4sGcVOpix7w61T+Xl5bh69SqSkpJw9+5dlJSUQCaTwdLSEo6OjujSpQv69OkDLy+v1i6V9JG9fU3YUxJFID8fyMwE7t5V//no+4KCph1HKq3Zr5ubbusnqgcDJdUr8V4xluy/pvpsZWqEnbMHISUmuhWrImq6+Ph47N27FxEREbh69SoUCkWjbRwcHDB69GiMHz8er732GszNzZ9ApfTUEYSap7odHIA+ferfrry8dsh89H1KCpCX9+TqJnoMAyXVqbhShve/u4xK2cP/8H41tQ+6O3dAA93EidoMqVSK77//Hv/6178QExOjWi6KYgOtHsrLy8Pu3buxe/duWFpaYsaMGfjoo4/g6+vbUiUT1c/CAujeveZVl927gRkznmxNRI9goKRaFAoRf9obizuSctWy90Z44w++rq1YFZFmqqqqsHnzZnz55ZfIyckBoB4iPT09MWjQILi6usLe3h52dnYwNzdHfn6+6nXjxg1cvXoVFRUVAIDS0lJs27YN27Ztw4QJE7By5Ur0aehqEhHRU4aBktSUVMrw2b5YHL+Ro1o2xNsBC8c824pVEWkmLCwMCxcuRFpamipEenh44LXXXsPLL78MPz8/dOyo2VBXcrkccXFxiIqKwsGDB3Hq1CnIZDIcPnwYR44cwcyZM7F27Vo4OTm15CkREbULOg2UmZmZDXZ079GjB0xMTHR5SNKhpJwSvPffy0i5X6Za5mJthq9n9IeRIQcEoLZv6tSpAABDQ0NMnjwZ7777LkaNGtWsfRkaGqJv377o27cv5s+fj/v37+P//u//sHnzZqSmpmLnzp3o0qULli1bpstTICJql7QKlAUFBThw4AAiIiJw5swZ5ObmNri9kZER+vbtq+roPmTIEG0OTzqSJinDzug72H0hAxWyhwPuOnUwxbaZA+HYgXN1U/tgaGiIoKAgLF68GJ6enjrdt6OjIz777DMEBwdj7969WLFihU73T0TUnjUrUJ44cQL/+te/cPToUchkMtXyxjq7y2QyXL58GZcvX8YXX3wBT09PBAUF4d1334WDg0NzSqFmEkUR51PzseNsKo4n5ODxX52fpz3++VZ/OHUwa50CiZohISEB3bp1a9FjGBgYYPr06Zg2bRrS09Nb9FhERO1FkwLlwYMHERISgmvXaoaSeTRAmpqaok+fPo12dM/KylK1SU1NxdKlS7Fq1Sq8++67WLJkCVxcXJp1Iqmpqfjll19w4cIFXLhwAfHx8ZDL5Vi1ahWWLl3a5P2FhIQ0egUiISEBPu1oeiuFQsSN7GJE3c7DodgsxGcV17ndnKFe+PM4HxjzNje1My0dJh9lYGCg86ugRETtlUaB8tKlS/jkk0/w+++/q0KkmZkZRo8erero3rdvXxhrMFF9dnY2Lly4gKioKBw6dAi3bt1CZWUlvvnmG2zbtg2LFy/GokWLYNrE2QM2btyIjRs3NqmNJjw8PNC5c+c611lYWOj8eLokiiJS88oQlSxB9O08nEuRoLBcVue2RgYCJvR1w5yhXujdyeYJV0pERETtmUaB0t/fH0BNQPHz88O7776LN954A5aWlk0+oKurKyZOnIiJEydi3bp1iImJwbZt2/Ddd9+hpKQEISEhEAShyVcVO3bsiPHjx8PPzw+DBg3Ctm3bEBYW1uT6HjdnzhyEtJP5UOUKERn55biaUYCo2zUhMquo4am3bC2M8ZZ/ZwQO8YSzNW9vExERUdNpFChFUcSYMWPw+eefY9iwYTotoF+/fvjnP/+JNWvWYNOmTfj73/+u0SwWj3s8gO7evVtXJbY51XIF0vLLkZRTitu5JUjKLcWtnFIk3y9FVXXj/9uZGhlgoKcdXunthkn93WFuYvgEqiYiIiJ9pVGgPHnyJEaMGNGihVhbW2PJkiWYP38+UlI4FwsAyOQKpEnKkJRT+iA0luB2bilS7pehSq556DYQgD6dbDG0mwOGdu2I57rYwcyYIZKeXnFxcYiNjUVubi7Kyso0/iOWQwQREdVNo0DZ0mHyUdbW1ujXr98TO15jTp48ifj4eEgkEtjb28PPzw+BgYHNfnhISa4QISmV4l5xJXKKH/wsqkROcSXuFVciu6gSaZIyyOSaTRP3uGecrRDQtSOGdusIf297WJs13r+VSN9t3boVX375JVJTU5vVnoGSiKhunCmnEb/99pva57CwMISEhGDTpk2YNWtWk/ZVWC7DxH+eRU6xFPdLpZArmhcWH+XUwRTdna3Q3anDw59OVrCz5ADyRI96++238eOPPwLQfD7vRwmCoOuSiIj0BgNlPVxdXfGXv/wFkyZNgre3N8zNzXH16lWsXr0aERERmDNnDhwcHDBhwgSN91kpkyP2blGz6nGxNnssONa8t7HglUeixuzYsQM//PCD6vPLL7+McePGwdvbG5aWlgyLRERaeiKBMiUlBTt37sTNmzdhbm6O559/Hu+8806ThwZ6kt57771aywICAnD06FFMnjwZ4eHhWLBgAcaPH1/vf4xCQ0MRGhqq0fHMDAFbU8DWVICtSc17R3MBbpYCXC0Bc6NqAIU1rwJAUgBIbmpxglqorKxEZGRk6xyc6sXfS/2++uorADVXGT///HMMHTpUtU4qlWq8H/7vS0RUN60C5d27d/Hmm28CAIKCghAYGFhrm8OHD+ONN95Q+9L+7rvv8I9//APHjx+Hm5ubNiU8cYIg4IsvvkB4eDiSk5Nx7do19O3bt85tg4ODERwcrPpsZe+MWQGecLExg7O1KZytzeBsbQYXazNYmrafi8WRkZF4+eWXW7sMegx/L/XLzMyEIAiYNGkSVq5c2drlEBHpHa1SzLFjx3D27FkIgoBdu3bVWn///n28/fbbqKysPRZiQkICJk+ejHPnzmlTQqt45plnYG9vj/z8fNy+fbveQPk4WwtjhLzaq4WrI6LHKe8iPPfcc61cCRGRftJqbr2ff/4ZANCzZ886pyD75ptvUFJSAkEQMGTIEOzZswd79uxBnz59AAAXLlzAwYMHtSmh1ShnBaqurm7lSoioMV5eXgCAioqKVq6EiEg/aXWFMikpCYIgwM/Pr871yicqnZ2d8csvv8Dc3BwAMGzYMHh6ekImk2Hv3r2YOHGiNmU8cXl5ecjNzQUAdOrUqZWrIaLGvPbaa4iNjcXp06dbuxQiIr2k1RXK+/fvA0Cdc12npaUhOTkZgiDg7bffVoVJAHBxccG4ceMgiiIuX76sTQmtIjQ0FKIowsbGBoMGDWrtcoioEfPnz4etrS2ioqJw5MiR1i6HiEjvaBUo8/LyAAAWFha11kVHR6vejxs3rtb6Z599FgCQlZWlTQla2bBhAzw9PTF9+nS15fHx8Zg/fz7i4+PVlldWVmLNmjX48ssvAQCLFy+GiQnHeyRq6xwdHREWFgZzc3NMnz4dO3bsaNZYlEREVDetbnkbGxtDJpOhsLCw1rqzZ8+qthk8eHCt9ba2tgBQ5wM7zREVFaV267y0tBQAsHbtWmzYsEG1/OrVq/Dw8AAAFBYWIi0trVb/T5lMhs2bN2Pz5s1wdHRUXYFNSEhAeXk5AGDu3LlYsmSJTmonopb3wgsv4OLFi5g9ezb++Mc/Yvny5Rg5ciQ8PDxgZmam0T44Uw4RUd20CpRubm64ffs2YmNja6376aefIAgCBg4cWOeXdVFRzQDfdV3dbA6ZTAaJRFJreXl5uSoEAoBcLm90X56enli1ahWio6ORmJiImzdvoqqqCk5OThg3bhyCgoI4PAtROxQdHY379+9DFEVkZWWpDXauCQZKIqK6aRUo/fz8kJSUhOPHjyMxMRE+Pj4AgPDwcKSlpUEQBLz44ot1tr15s2ZUbl091DJy5Mgm38IKCQlBSEhIreW2trZYunSpTuoiorbhvffew7Zt21Sfm/p9wdl0iIjqp1WgfOedd/D999+juroaw4YNw1tvvYWKigp89913AAADAwO8/fbbdbb9/fffIQgCevXiuIxE1LL27duHrVu3qj7/4Q9/wNixY5/o1Iupqan45ZdfcOHCBVy4cAHx8fGQy+VYtWpVo3/Anjt3Dl988QWio6NRWloKLy8vzJgxAwsXLmzwdn1mZiZWrFiBiIgI5ObmwtnZGWPHjsWyZcvg7u6u61MkoqeYVoFyzJgxmDRpEsLDw5Gfn49vvvkGwMO//N9991107969VruoqChkZ2dDEAS1KdCIiFqC8rvJwMAAYWFhrTJU2caNG7Fx48Ymt/v+++8xc+ZMyOVyuLu7w8PDA3FxcVi2bBkOHz6MU6dO1dl16MaNGxg2bBjy8/NhY2MDX19fJCcnY8uWLQgLC8PZs2dVd5WIiLSl1VPeQM1Ykx988AFMTEwgiiJEUYSxsTE+/PBDtYdhHrV582bVe/ZFJKKWlpCQAEEQ8Nprr7XauLcdO3bE+PHjsXLlSkRERGDy5MmNtrlz5w7mzp0LuVyOdevWISMjA1euXEFSUhKeffZZXLx4EYsWLarVTi6XY+rUqcjPz8fkyZORlZWFy5cvIzMzE6+//jokEgneeOMNKBSKljhVInoKaT2BtImJCb755ht88cUXSExMhEKhgI+PDzp06FBvm3HjxmH06NEwNjZWDR9ERNRSqqqqALTu1IuP39bevXt3o23Wr18PqVSKMWPGYOHCharlXbp0wY4dOzB06FBs2bIFf/3rX+Hs7Kxav3//fty4cQMODg74z3/+o7qCaWlpiZ07d+L06dO4du0aDh48iEmTJunoDInoaab1FUolS0tLDBgwAIMGDWowTALAm2++iZkzZ+LNN9/U1eGJiOqlHPqrPU29KIoiwsPDAdQMU/a4gIAA+Pj4QCaT1ZrCdv/+/QCAadOm1fo+7tChA6ZOnQqgpm8pEZEu6CxQEhG1VRMmTIAoijhz5kxrl6Kx9PR0ZGdnA0C9fc2Vy8+fP6+2/Pfff29WOyKi5tIqUB4+fBjp6em6qoWIqEV89NFH6NixI86cOYOjR4+2djkaSUpKAgCYmprCzc2tzm28vb3VtgVqbu8rv5eV6+trd+fOHchkMp3VTERPL60C5cSJE+Hl5QV7e3u88MILWLBgAXbu3ImYmJhGv6RWrFiBDRs2IDMzU5sSiIga5erqirCwMNja2uKNN97A9u3b2/wDKQUFBQBqxsWtb1gjOzs7tW2BmkkjlOemXF9fO4VCgeLiYp3VTERPL60fyhFFEYWFhfjtt9/w22+/PdyxkRF8fHzQr18/9O3bV/XTwcEBAHD//n2sXLkS165dw44dO7Qtg4ioXitXrgRQ80fwzp078e6772L58uV44YUX2uzUi8ppaU1MTOrdxtTUFIB639BHp7Otr62y3eNtHxUaGorQ0FDVZ+V0tkREddEqUK5ZswYxMTG4evUqkpOT1f7il8lkiIuLQ1xcHP773/+qlru5ucHDwwMxMTEQRRHHjh3TpgQiokaFhISorvIpf2ZnZ7fpqReVIVf5hHpdpFIpAMDc3LxWu4baKts93vZRwcHBCA4OVn3W1axmRKSftAqUS5YsUb0vKytDbGysKmDGxMQgLi5O7YsLqJm5ISsrSzX4+ePriYhaQlOnWnzck556UXlburCwEKIo1nl85a3uR29t29jYwMDAAAqFQu1WeF3tDAwMYG1trevSiegppPUtbyVLS0sEBAQgICBAtUwulyMhIQEXL17Er7/+igMHDqC8vFy1XhAEtbHViIhawsmTJ1u7hCZTzjImlUqRlZVV51SJKSkpatsCNbe5O3fujDt37iAlJQVDhgypt52npyeMjY1bonwiesroLFDWxdDQEL6+vvD19cXs2bORn5+P4OBg7Nq1Cx4eHjhy5Ah69+7dkiUQEWHEiBGtXUKTde7cGS4uLrh37x6ioqIwbdq0WttERUUBAPz9/dWW+/v7486dO4iKisJbb72lcTsiouZ6ouNQ2tvbY+fOnZg/fz4yMjIwb968Nv+kJRFRaxAEQTWLzfbt22utj46ORmJiIoyNjfHqq6+qrXv99dcBAHv37kVJSYnaupKSEtWA5lOmTGmJ0onoKdQqA5uvW7cODg4OOHfuHDZt2tQaJRARtXkLFy6EiYkJfv75Z6xfv17VDzQtLQ1z5swBAAQFBcHFxUWt3eTJk+Hj4wOJRILZs2eruhqVlZVh9uzZkEgk8PX1xWuvvfZkT4iI9FarBEoLCwuMGTMGoiiqPQFORKSvoqKi0LFjR9VLOZf32rVr1ZZnZGSo2nh5eWHr1q0wMDDAokWL4OHhgeeeew7du3fHzZs3MWDAAKxfv77WsQwNDbFv3z7Y2dkhLCwMbm5uGDhwINzd3REWFgZ7e3vs2bMHBgacLI2IdKPVvk08PT0BADdv3mytEohIz+Tk5LTZ48lkMkgkEtVLOcJFeXm52nK5XK7WLjAwEGfOnMH48eNRUVGBGzduwNvbGyEhITh79iwsLS3rPJ6vry9iY2MRFBQEKysrXL9+HVZWVvjjH/+I2NhY9OzZs/knTkT0mBZ9KKchymErtB3Kg4hIqVu3bvjkk0+wYMEC1SQKLSEyMhLLli3DK6+8ovHYlCNHjmz2911AQAAOHz7c5HYeHh7YunVrs45JRNQUWl2h7Nq1KyZPnozVq1fjyJEjuHv3rsZto6KiIAgCn/ImIp0pKyvD2rVr0blzZ8yfPx8xMTE63ff27dvRr18/jBs3DpcuXdLZvomI2jutrlCmpqbizp07OHDggGqZvb09+vXrh379+qF///7o168ffHx8VH11RFHEX//6V1y/fh2CIOBPf/qTdmdARPTAli1bsHTpUuTm5uLf//43/v3vf6N37954/fXXMXr0aDz33HNq0w42Jj09HdHR0Th48CCOHj2KsrIy1VXGSZMmqR6MISJ62mkVKG1sbFBUVKS2TCKR4MSJEzhx4oRqmampKbp37w4rKyvcunUL+fn5sLOzw5o1a/iUIRHpTFBQEKZNm4b169fj66+/RklJCa5fv47r169jxYoVMDIyQq9evTBw4EC4urrCzs4O9vb2MDU1RWFhIfLz85Gfn6+akCEvL0+1b2WQHDZsGFatWoXhw4e31mkSEbU5WgXKgoICZGRkIDY2FrGxsbh27RpiY2Nx+/ZttfElKysrERcXB+Dhl3JhYSHWrl2LI0eOwNfXF71794avry969OjBmRuIqNmsra2xatUqBAcH49tvv8W3336renJaJpOpvq8a82h/RyMjI7z66qv49NNP8fzzz7dY7URE7ZXWD+V4eHjAw8MD48ePVy2rqKjA9evXVQEzNjYW169fV7uaKYoi0tPTkZGRgZ9++km13NDQEN27d0fv3r3Ru3dvfP7559qWSERPITs7O/z5z3/GkiVLcPLkSfzvf/9DZGQkUlNTNWpvbm6O4cOHY8KECZg2bRo6duzYwhUTEbVfLfKUt7m5Ofz8/ODn56e2PC0trdbVzOTkZLUrAdXV1UhMTERiYiL27dvHQElEWhEEAaNGjcKoUaMAAFlZWbh69SqSkpKQmZmJkpISVFdXw8LCAo6OjujSpYvqD1ojo1YbCIOIqF15ot+WXbp0QZcuXdSmCSsvL8f169dVQVN5NbOkpASCIDzJ8ojoKeDm5gY3N7fWLoOISK+0+p/fFhYW8Pf3h7+/v9ry1NRUjfo5EREREVHravVAWR8vLy94eXm1dhlERERE1AiNBjZvzgwNzXX//n2cP3/+iR2PiIiIiLSjUaCcOHEinn/+eURERLRYITk5Ofjzn/+Mrl27IjIyssWOQ0RERES6pVGgNDIywrlz5zB+/Hj06tULGzduhEQi0UkBp0+fxjvvvANPT0+sW7cOpaWlMDEx0cm+iYiIiKjlaRQor127hpdeegmiKCIhIQHBwcFwdXXFqFGjsHbtWvz666+1ZsypT0JCAnbt2oX3338f7u7uGDVqFH744QdIpVLY2tpi48aNWLhwoVYnRURERERPjkYP5fj4+CAyMhK//vorli9fjujoaFRXV+P06dM4ffo0gJqx3ry9vRuczuzWrVsoLS1V7Vc5/qSNjQ0++ugj/OlPf4KNjU0LnCYRERERtZQmPeX94osv4sUXX8Tvv/+Ob775BgcPHkR5eTmAmnCYnJyM5OTkets/OoA5APTp0wdBQUGYNWsWrKysmlE+EREREbW2Zg0bNHjwYAwePBhlZWU4evQojh07hrNnz9aa9eZxNjY2GDhwIF566SVMmDABPXv2bHbhRERERNQ2aDUOpaWlJaZNm4Zp06YBAEpLS3H79u16pzPr3LmzToomIiIiorZDpwObW1lZoV+/fujXr58ud0tEREREbZhGT3kTEREREdWHgZKIiIiItNJm5/ImImpJWVlZyM3NRWlpKRQKhUZthg8f3sJVERG1TwyURPTUSEpKwvr163HgwIEmz/YlCAKqq6tbqDIiovaNgZKIngo//vgj5s6dC6lU2uDwZkRE1HQMlESk965fv47AwEDI5XIAgIWFBUaNGoVnnnkGVlZWEAShlSskImrfGCiJSO999dVXkMvlEAQB06ZNw7fffstpXomIdEgvnvIWRRFnz57FwoULMXjwYNja2sLExARubm6YPHkyTp482ex9nzt3DhMnToSjoyPMzc3Rs2dPrFq1CpWVlTo8AyJqSadOnYIgCOjevTu+//57hkkiIh3Ti0B54sQJDBs2DF999RUuXrwIZ2dn+Pr6oqSkBPv378eoUaPw17/+tcn7/f777zFs2DAcOnQIpqam6NGjB27fvo1ly5Zh+PDhqnnMiahty83NBQBMnDgRBgZ68bVHRNSm6MU3qyiK6NatGzZt2oS8vDzcvHkTV65cgUQiwZ///GcAwOrVq3HkyBGN93nnzh3MnTsXcrkc69atQ0ZGBq5cuYKkpCQ8++yzuHjxIhYtWtRSp0REOmRrawsAsLOza+VKiIj0k14ESj8/PyQkJGDevHlq/8EwMTHBmjVrMHbsWADA1q1bNd7n+vXrIZVKMWbMGCxcuFDVab9Lly7YsWMHAGDLli3IycnR4ZkQUUvo0aMHACAjI6OVKyEi0k96ESitra1hZFT/80WjR48GANy6dUuj/YmiiPDwcADA3Llza60PCAiAj48PZDIZDh482IyKiehJCgwMhCiKOHz4MGQyWWuXQ0Skd3QeKIuLi/Hbb78hLCwM3333HRISEnR9iCZTPkBjbm6u0fbp6enIzs4GAAwdOrTObZTLz58/r4MKiaglBQYGYsSIEbh79y6WLFnS2uUQEekdnQXKiIgIjBw5Eg4ODnjhhRcwbdo0zJo1CxEREbW2nTFjBsaMeVuYiwAAIABJREFUGYNPP/1UV4evlyiK2LdvH4D6w+HjkpKSAACmpqZwc3Orcxtvb2+1bYmo7TIwMEBYWBhGjBiBDRs2YOrUqYiPj2/tsoiI9IbW41AqFAq8//772L59OwCozUBR32DBPXv2xPLly3HixAl89tln6NSpk7Zl1Gvr1q24evUqTExMNA6wBQUFAGo68td3Dsq+mspt6xIaGorQ0FDV58LCQkRGRmpaeptVWVmpF+ehb/h7ARYvXtzgerlcDlEUsX//fuzfvx/29vZwdHSEiYlJo/sWBAFffPGFrkolItIrWgfKjz/+GNu2bQMA2NjYYPr06ejTpw8++OCDetvMmjULISEhEEURhw4dwvz587Uto05XrlzBJ598AqDmKe+uXbtq1E55i7yh/8iYmpoCACoqKurdJjg4GMHBwarPnTp1wssvv6xRDW1ZZGSkXpyHvuHvBRg7dmyjs94o14uiiPz8fOTn5ze6X1EUIQjCU/+/LxFRfbS65X3p0iVs2rQJgiDA398ft27dwubNmzFv3rwG23l4eKBPnz4AoNWg4w1JTU3F+PHjUVlZiTfffBOfffaZxm3NzMwAAFVVVfVuI5VKAWjeL5OIngxRFDV6NXVbIiKqn1aBcsuWLQBqQlV4eDgcHR01bjtw4ECIotgi/Zju3buH0aNHIzs7G6+88gp27tzZpLl6lbezCwsL6/2PifJWN8e1I2o7FApFi72U84ATEVFtWgVK5XRmEydOhIuLS5Pauru7AwCysrK0KaGW/Px8jB49GsnJyRgxYgT27dsHY2PjJu2je/fuAGquQtZXX0pKitq2RERERE8rrQKlcmidnj17Nrmt8laxLqcvLC0txbhx4xAXF4dBgwbh8OHDzbol3blzZ1VAjoqKqnMb5XJ/f//mF0xERESkB7QKlNr0LZJIJABqHuTRBalUiokTJ+L8+fPo1asXjh07hg4dOjRrX4IgYNKkSQCgenr9UdHR0UhMTISxsTFeffVVreomopa3a9cu7Nq1q1nj4t66dUvVnoiI6qZVoHR2dgYAJCcnN7mtckBw5a1vbcjlckyfPh0nTpxA165dcfz4cdjb2zfabsOGDfD09MT06dNrrVu4cCFMTEzw888/Y/369arwnJaWhjlz5gAAgoKCmnyrn4ievFmzZmH27Nl1jovbmOPHj2PWrFmqf/dERFSbVsMGDR48GKmpqYiIiIBMJtO4r2J8fDzOnj0LQRAwfPhwbUoAAOzduxcHDhwAUDOA8dSpU+vcztXVVTXIOVDz0E1aWho8PT1rbevl5YWtW7di9uzZWLRoETZu3AgnJyfExcVBJpNhwIABWL9+vda1E1H7wKe9iYjqp1WgnDJlCn788Ufk5uZixYoVWL16daNtioqK8NZbb6nGdavr6mBTKYfwAWpmrqlv9pouXbo0ab+BgYHo1q0b1q5di+joaNy4cQPe3t6YMWMGFi9erBpeiIj0lzJINmWkCCKip41Wt7wnTZqEAQMGQBRFrF27FgsWLEBRUVGd2yoUCoSHh2PgwIG4du0aBEHA6NGjERAQoE0JAGpuZ2kyltydO3fU2ikHVz916lS9+w4ICMDhw4chkUhQWVmJxMRELF++nGGS6CmhHPjcysqqlSshImq7tJ7Le+/evXBycoIoivj666/h4uKC559/XrV+z549GDt2LBwdHTFlyhTVcDvu7u7s5E5EbZpUKkV4eDiAmm4wRERUN62nXvTy8sKZM2fwxhtvICYmBlKpFOfOnVPdHrp06RIA9f5H/fr1Q3h4OJycnLQ9PBGRmoMHD+LgwYN1rtuzZw/i4uIa3YdcLkd+fj4uXLiAvLw8CIKAF198UdelEhHpDa0DJVAzuPeFCxfwww8/YMuWLbh48SJkMpnaNgYGBujfvz/ef/99zJw5E0ZGOjk0EZGamJiYOmfHEkURly5dUv2RqwnlH8IdO3ZEcHCwTuskItInOkt1RkZGCAwMRGBgICoqKpCYmAiJRILq6mo4ODige/fusLW11dXhiIgaVNdT2U19UtvV1RV/+MMfsGzZMri5uemqNCIivdMilwnNzc3Rv3//ltg1EVGDPv30U8yaNUv1WRRFeHt7QxAE/OUvf0FQUFCj+zA2Noa1tTUfxCEi0hDvOxORXrGxsalzBi5RFGFnZ9fk4cOIiKhxDJREpPdOnjwJAOjatWsrV0JEpJ8YKIlI740YMaK1SyAi0ms6C5QVFRX49ddfERsbi9zcXJSVlUGhUDTaThAEbN++XVdlEBE1SXV1NQoKCgAAdnZ2HIGCiKgZtP7mrKqqwooVK/DNN9+grKysWftgoCSiJykiIgLfffcdfv/9d6Slpamt69y5M4YMGYK3334bY8eO5ZSLREQa0CpQVldXY+zYsTh16lSTh+NQ4pc1ET0p165dw9y5c3HlyhXVsse/u9LT05Geno49e/agf//+2L59O/r27fukSyUiale0CpQbN25UdXY3MTHB7NmzMW7cOHh7e8PS0pJhkYjajNOnT2PChAkoKytTC5F2dnZwcHCAKIrIz89X3f4GgCtXruD555/HkSNH2A+TiKgBWgXK//73vwAAU1NTnDp1Cv7+/jopiohIl/Ly8jB16lSUlpYCANzd3bF48WJMmjQJ7u7uattmZmZi//79+Oqrr5CRkYGysjJMmTIFCQkJ6NixY2uUT0TU5hlo0zgpKQmCIGDatGkMk0TUZv3jH/9Qzcn90ksvISEhAR9++GGtMAnUhM2PPvoICQkJGD16NAAgPz8f//jHP5502URE7YZWgdLMzAwA0KNHD50UQ0TUEg4dOgQAcHBwwL59+zSaAcfCwgJ79+5VXZU8ePBgi9ZIRNSeaRUolYMEFxYW6qQYIqKWcOfOHQiCgNdff73OWXTqY2Njg9dffx2iKNZ6GpyIiB7SKlBOnz4doiji+PHjuqqHiEjnTExMAACenp5NbqucqtHY2FiXJRER6RWtAmVQUBA6deqEmJgYbNu2TVc1ERHplDJI5ubmNrltXl6e2j6IiKg2rQJlhw4dcPToUTg5OWHevHlYtmwZioqKdFUbEZFOTJ48GaIo4vDhw5DL5Rq3k8vlOHTokOp2ORER1U3rmXJ69+6NmJgYzJ07F3/729/w1VdfYcCAAfDw8FA9tNMQTr1IRC1t3rx52LJlC1JSUrBw4UKEhoZq1G7RokVITk5G586dMX/+/Baukoio/dI6UFZXV+Of//wnoqKiAACVlZWIjo5u0j4YKImoJdnZ2eHw4cOYOHEiNm7ciJs3b2LFihUYOHBgndtfunQJISEhiIiIgJeXFw4cOAB7e/snXDURUfuh9dSLr732GiIiIgA8nMKsKdMwcjYdImppc+bMAQD069cPaWlpOHbsGI4dOwYXFxf06dMHDg4OAACJRILr168jOzsbQM33U79+/Rodg5J3WojoaadVoNyxYwd++uknADWz5cyePRtjx47l1ItE1Kbs3Lmz1veRKIq4d+8e7t27V2t7URQhCAJEUcSBAwc0OgYDJRE9zbQKlMonu01NTXH69Gn4+fnppCgiIl2r685JQ3dTeKeFiEhzWgXKR6deZJgkorYqNTW1tUsgItJrWgVK5V/lPj4+OimGiKglKAcnJyKilqHVOJReXl4AgOLiYp0UQ0RERETtj1aBUjlYMKdeJCIiInp6aRUoP/jgA3h5eeHq1aucepGI2pXi4mL89ttvCAsLw3fffYeEhITWLomIqN3Sqg+ljY0NDh8+jFdffRXz5s1DWloaPvvsM9jY2OiqPiIinYqIiMCXX36JqKgoKBQK1fL169ejR48eatvOmDEDEokEPXv2xIYNG550qURE7YZWgVI5WHCfPn2QkpKCNWvW4O9//zunXiSiNkehUOD9999Xfd88OixQfcP+9OzZE8uXL8eJEyfw2WefoVOnTk+kViKi9karQPnoYMHKn5x6kYjaoo8//ljVNcfGxgbTp09Hnz598MEHH9TbZtasWQgJCYEoijh06BDn8yYiqodWfSiBmr/yH33VtayhFxFRS7t06RI2bdoEQRDg7++PW7duYfPmzZg3b16D7Tw8PNCnTx8AwMmTJ59EqURE7ZJWVyg5WDARtQdbtmwBAJibmyM8PByOjo4atx04cCBiY2MRHx/fUuUREbV7WgVKDhZMRO3BqVOnIAgCJk6cCBcXlya1dXd3BwBkZWW1RGlERHpB61veRERtXXZ2NoCah2yaytzcHABQXl6u05qIiPQJAyUR6T1t+mtLJBIA4HBoREQNYKAkIr3n7OwMAEhOTm5y2/PnzwN4eOubiIhq06gPpXK8SUB93MhHlzcXx6EkopY2ePBgpKamIiIiAjKZDMbGxhq1i4+Px9mzZyEIAoYPH65VDampqfjll19w4cIFXLhwAfHx8ZDL5Vi1ahWWLl1aZ5uQkBCsWLGiwf0mJCTAx8enznWZmZlYsWIFIiIikJubC2dnZ4wdOxbLli1jQCYindIoUD463iTwcNzIx5c3FwMlEbWkKVOm4Mcff0Rubi5WrFiB1atXN9qmqKgIb731FkRRhCAImD59ulY1bNy4ERs3bmxWWw8PD3Tu3LnOdRYWFnUuv3HjBoYNG4b8/HzY2NjA19cXycnJ2LJlC8LCwnD27Nl6gygRUVNp/JS3sg/S4wFS27EkdRFIiYgaMmnSJAwYMACXL1/G2rVrUVZWhpCQkDr7RSoUChw8eBCLFi1CcnIyBEHA6NGjERAQoFUNHTt2xPjx4+Hn54dBgwZh27ZtCAsL06jtnDlzEBISovGx5HI5pk6divz8fEyePBm7du2ChYUFysrKEBgYiP379+ONN97A1atXYWDAnk9EpD2NAmV9401yHEoiai/27t2LIUOGIDc3F19//TW+/fZbDBgwQLV+z549OH78OC5cuIDCwkLVcnd3d+zatUvr4z9+W3v37t1a77M++/fvx40bN+Dg4ID//Oc/qquYlpaW2LlzJ06fPo1r167h4MGDmDRpUovVQURPD40CZX3jTbaVcShFUURUVBQOHjyIM2fOIDExEeXl5ejYsSOGDBmCDz/8EC+88EKT9qlt3yUialu8vLxw5swZvPHGG4iJiYFUKsW5c+dUd0kuXboEQP2uS79+/RAeHg4nJ6dWqbm59u/fDwCYNm0aOnTooLauQ4cOmDp1Kr799lvs27ePgZKIdELjW97p6ekAAHt7e1hZWbVYQc1x4sQJvPTSSwAAAwMDdOvWDZaWlkhKSsL+/fuxf/9+LF26FKtWrWryvpvTd4mI2qbu3bvjwoUL+OGHH7BlyxZcvHgRMplMbRsDAwP0798f77//PmbOnAkjI63mf9CJkydPIj4+HhKJBPb29vDz80NgYGC9g7T//vvvAIChQ4fWuX7o0KH49ttvVU+wExFpS+NvSk9PTwiCgPXr1yM4OLgla2oyURTRrVs3BAcHY/r06bCzswMAVFVVISQkBGvXrsXq1avh7++P8ePHN2nfTe27RERtm5GREQIDAxEYGIiKigokJiZCIpGguroaDg4O6N69O2xtbVu7TDW//fab2uewsDCEhIRg06ZNmDVrltq6qqoq1QUAb2/vOvenXH7nzp0mPfVORFSf1v/TWwf8/PyQkJBQ60qCiYkJ1qxZg5iYGERERGDr1q1NDpREpL/Mzc3Rv3//1i6jXq6urvjLX/6CSZMmwdvbG+bm5rh69SpWr16NiIgIzJkzBw4ODpgwYYKqTVFRERQKBQCo/rh+nHK5QqFAcXExHBwcWv5kiEiv6cXjfdbW1g3elho9ejQA4NatW0+qJCIirb333nv429/+hoEDB8Le3h7m5uYICAjA0aNHMWnSJIiiiAULFqj1+6ysrFS9NzExqXO/pqamqvcVFRV1bhMaGopOnTqpXqWlpTo6KyLSR3oRKBuj/IJVzsnbFCdPnsTUqVMxatQoTJkyBevWrcO9e/d0XSIRPWGlpaXIzs5GWVlZa5fSZIIg4IsvvgBQM/vPtWvXVOvMzMxU76uqqupsL5VKVe/r+14MDg7G3bt3Va+21neeiNoWvbjl3RBRFLFv3z4A9XdQb0hT+i4RUdtUVVWFPXv24MSJEzhz5gwyMjJQXV2tWm9sbAwPDw8MHz4co0aNwrRp09p8v8JnnnkG9vb2yM/Px+3bt9G3b18ANXOOGxgYQKFQoKCgoM62yuUGBgawtrZ+YjUTkf7S+yuUW7duxdWrV2FiYoJPP/1U43bKvksXL16ERCJBeXk5oqKiMHbsWFRUVGDOnDk4fPhwC1ZORLqwefNmeHt7Y9asWdi1axdSU1Mhk8kgiqLqVVVVhZSUFOzcuROBgYHw9vbGli1bWrv0RilD76Ph2MTERDUyRUpKSp3tlMs9PT3bfHAmovZBr69QXrlyBZ988gkAYPXq1ejatavGbd97771ay5R9lyZPnozw8HAsWLAA48ePr3e2n9DQUISGhqo+FxYWIjIysoln0fZUVlbqxXnoG/5e1FVXV2PDhg04ceKEallDM3s9OhtYZmYm5s2bh7CwMHzyyScwNDRs8XqbKi8vD7m5uQCATp06qa3z9/fHnTt3EBUVhbfeeqtW26ioKNV2RES6oLeBMjU1FePHj0dlZSXefPNNfPbZZzrZr7LvUnh4uKrvkvJW0+OCg4PVhljq1KkTXn75ZZ3U0ZoiIyP14jz0DX8v6j788EP8+uuvEAQBoijC2NgYEyZMwLBhw9C3b1/Y2dnBysoKJSUlKCgoQGxsLM6cOYMjR46ormD+8ssv6NGjBzZs2NDap1NLaGgoRFGEjY0NBg0apLbu9ddfx549e7B37158+eWXaoObl5SUqLoBTZky5YnWTER6TNSQIAiigYGBzl+GhoaalqCx7OxssWvXriIA8ZVXXhGrqqp0fgx7e3sRgPi///1P4zbu7u46r6M1HDt2rLVLoDrw9/LQr7/+qvqOEQRBfPvtt8WsrCyN2mZlZYlvvfWWKAiCKAiCaGhoKJ4+fbpZdTT0b37mzJkiAHHVqlV1ro+LixPnzZsnxsXFqS2vqKgQ//a3v4kGBgYiAHHNmjW12lZXV4s+Pj4iAHHy5MliWVmZKIqiWFpaKk6ePFkEIPr6+opyuVwn50JtwI8/iiKg/srMbO2qqJ1ryr/7JgdK5Zesrl4GBgbNOsn6SCQS0dfXVwQgjhgxQiwvL9fp/pWcnZ1FAOLu3bs1bqMvX8gMLm0Tfy8PjR49WvX9snz58mbtY9myZap9vPzyy83ax6P/5s+ePSs6ODioXqampiIA0cLCQm15enq6KIqiePXqVRGACEB0dHQUBwwYIA4YMEC0sLBQLZ87d66oUCjqPPb169dFOzs7EYBoY2MjDhgwQLSxsREBiPb29mJ8fHyzz4XaIAZKagFN+XevVw/llJaWYty4cYiLi8OgQYNw+PDhZg0V1JiG+i4RUetKTU1V3eoeOXJks2e6WrFiBUaOHKm69Z2WlqZVXTKZDBKJRPVSDt1TXl6utlwulwOoeWBm1apVGDt2LKysrHDz5k1cv34d9vb2mDJlCo4dO4Zt27bV24fb19cXsbGxCAoKgpWVFa5fvw4rKyv88Y9/RGxsLHr27KnV+RARParJfSg///xzBAUFtUQtWpFKpZg4cSLOnz+PXr164dixY2r9hnSpob5LRNS6IiMjIYoiBEHA559/rtW+li5dilOnTkEURRw7dqzOh/U0pQynmrK1tcXSpUubfTwA8PDwwNatW7XaBxGRJpocKO3s7NClS5eWqKXZ5HI5pk+fjhMnTqBr1644fvw47O3tG223YcMGbNiwAYMHD8bu3btVy+Pj4/Gvf/0LH3zwAXr16qVaXllZidDQUHz55ZcAgMWLF9c7EwURtY7Lly8DAJycnDBq1Cit9jVq1Cg4OzsjNzdXtV8iIqpNL57y3rt3Lw4cOACgZqDeqVOn1rmdq6ur6ulGoGYYn7S0NHh6eqptJ5PJsHnzZmzevBmOjo6qMd0SEhJQXl4OAJg7dy6WLFnSAmdDRNqIjY2FIAg6GxLH398fhw4dQkxMjE72R0Skj/QiUD46jVhSUhKSkpLq3E7TK6vKvkvR0dFITEzEzZs3UVVVBScnJ4wbNw5BQUEcnoWojcrLywOAJo0725Bu3boBAO7fv6+T/RER6SO9CJSzZs1q1jSIISEhdXbY10XfJSJqHUVFRQBq/h3rgo2Njdp+iYioNr16ypuIqLi4GABgaWmpk/1ZWFgAqBlFgoiI6sZASUR6RTnsTnvZLxGRPmhSoGzKkBdERERE9HTQuA9lamoqAGg0HA8RUWsrKChAenq6TvZDREQN0zhQtrWxJ4mIGrJmzRqsWbOmtcsgInoq6MVT3kREddFFN536pjYkIqKHGCiJSO/osr83+44TETWOgZKI9IpCoWjtEoiInjocNoiIiIiItMJASURERERaYaAkIiIiIq0wUBIRERGRVhgoiYiIiEgrDJREREREpBUGSiIiIiLSCgMlEREREWmFgZKIiIiItMJASURERERaYaAkIiIiIq0wUBIRERGRVhgoiYiIiEgrDJREREREpBUGSiIiIiLSCgMlEREREWmFgZKIiIiItMJA+QTJ5Arczi1BXqkU1XJFa5dDREREpBNGrV3A00RSWoWXQn9Tfe5gagRbS2PYWZjA1sIEtubGsLMwhq2FieqnrYVyfc1nazMjCILQimdBREREpI6BshWVSKtRIq1GRn6Fxm0MDQTYmhvD5kHQVAVPc2PYWT4SQM0fBFNLY9iam8DcxLAFz4SIiIieZgyU7YxcIUJSVgVJWRWAMo3bmRoZoKOVKZysTeHcwQzO1qZwtjF78L7ms5O1Ga+AEhERUZMxUD5BjtamOPrx8ygsl6GwXIaC8ioUlleh4MHnmvdVKKx4+Fkh6ubY0moFMgsrkFnY8NVQc2NDVbh0tjaDcwdTOFubwcnaFC4PllXJdVQUERER6QUGyifIUBDQy81G4+0VChElldUorKgJnQXlVSh68LOgXIai8ofLC8tlKKyoQmGZDCXS6mbXWCGT446kHHck5Q1u95cLkaorm84PgmYnO3N0trdAZ3sLuNmaw9iQz3wRERE9DRgo2zADAwE2FjX9Jbs4aN5OJlegqEKmuvpZUFYTOO+XSpFbXImcYilySiqRWyxFTnElqptxGbSkshollaW4nVta53pDAwFutmaqgNnZ3vKR9xawsTBu8jGJiIiobWKg1EPGhjX9JTtamTa6rUIhIr+8CjnFDwPmvQehM7e4EjklNe/zSqUQm5A75QoRGfkVyMivQBQktdbbmBurwqWHvQW6OFioXd00NGA/TiIiovaCgfIpZ2AgqMJnL7f6t5PJFcgrlSKnWIrI0+fg1rWHKnjmFFfiXlElMgrKUSnTbHzNogoZrmcW4XpmUa11pkYG6O5shWecO+BZ5w54xqXmp6uNGR8YIiIiaoMYKEkjxoYGcLUxh6uNOXIcDfDyEM9a24iiiPslUqTnlz98SR6+zy2RanQsabUCcZnFiMssVlvewdQI3Z2t8KxLB7WwqcmVWCIiImo5DJSkM4IgwMnaDE7WZhjoaV9rfUWVHBkF6iHz0VdVdcNXN0uk1biSXogr6YVqyx0sTfCMcwf0crNG/852eK6LLVxtzHV6bkRERFQ/Bkp6YsxNDPGMc83VxccpFCLul0qRmleGpNxS3LpXgps5Jbh5rwRFFbIG9yspq8K5FAnOpUgApAIAXG3M0L+zLZ7rbIf+ne3g624NUyMO7k5ERNQSGCipTTAwEFTDDw32fvhIu/I2+s2cEtzKeRg0k3JKUFYlr3d/2UWVyL5+Dz9dvwcAMDE0QE83azz34Aqmv5cDHDvwVjkREZEuMFBSm/bobfRh3R1VyxUKEZmFFUjKLcHNe6VIyC5GTEYh0vPrHj+zSq5ATEYhYjIKsSOqZllfD1u86OOEF3s4oaerNR/4ISIiaiYGSmqXDAwEeDwYcmiUj7Nq+f0SKWIyCnElvQBX0gpw7W4RKmR1X8mMzShEbEYhQo/fgquNGUY9CJcBXTvCzJi3x4mIiDTFQEl6xbGDKUb3dMbonjUhs1quQOK9ElxNL3jwQE8B0uqYBSi7qBLfn0/H9+fTYWZsgFE+TpgV4IVBnna8cklERNQIBkrSa0aGBvB1t4Gvuw3eGVKz7F5RJU7ezMWvCTk4ezuv1tiZlTIFfnrQ/9LX3RpzhnphfB83mBhxKkkiIqK6MFDSU8fFxgwz/Dpjhl9nVMrkOJcswS8JOTiRmIvsokq1beMyixG8NxZrIxLxzuAumDXUE9ZmnDaSiIjoUXpzyeXAgQN47733MGDAALi6usLExAS2trYICAjAxo0bUVVV1az9njt3DhMnToSjoyPMzc3Rs2dPrFq1CpWVlY03pjbPzNgQL/g44W+TeiN6ySj89PEwvD+iK2wfm2v8fokUocdv4ZWvzyA+q/bsPkRERE8zvQmUX331FbZs2YL4+HiYm5ujb9++sLKywrlz5/Dpp58iICAAhYWFje/oEd9//z2GDRuGQ4cOwdTUFD169MDt27exbNkyDB8+HOXldT9RTO2TIAjo6WaNJWN9cG7Ji1gzqTe6OVmpbZORX4HXN0Uj/OrdVqqSiIio7dGbQBkUFISTJ0+ipKQEKSkpuHjxIu7evYtz586hU6dOuHz5Mj7//HON93fnzh3MnTsXcrkc69atQ0ZGBq5cuYKkpCQ8++yzuHjxIhYtWtSCZ0StydzEEG/6d8bxBcOxa44f/B6Z+UdarcCCPbHYfCq5FSskIiJqO/QmUM6aNQsjR46EsbH6rcrBgwcjNDQUQM1tcU2tX78eUqkUY8aMwcKFC1VP+nbp0gU7duwAAGzZsgU5OTk6OgNqiwRBwPBnHPHju4Mxf2RXtXV///kmCsub15WCiIhIn+hNoGyIj48PAGh8i1oURYSHhwMA5s6dW2t9QEAAfHx8IJPJcPDgQd0VSm0VyCoGAAAYo0lEQVSWoYGARX/wwaa3nlMtq1aIiLotacWqiIiI2oanIlCeO3cOAPDcc881smWN9PR0ZGdnAwCGDh1a5zbK5efPn9dBhdRejOvtCjcbM9XnqOS8VqyGiIiobdDbYYPkcjmys7Nx6NAhLFmyBJaWlli7dq1GbZOSkgAApqamcHNzq3Mbb29vtW3p6RBxPRtZjwwtxCHPiYiI9DBQbtiwAQsWLFBb9tprr2HVqlXw9fXVaB8FBQUAAFtb23pnSbGzs1Pbti6hoaGq/psAUFhYiMjISI1qaMsqKyv14jyaQiGKiMoW8b/bDwdBNzMEegqZiIzMasXKHnoafy9ERNQ26F2gdHd3x9ChQyGTyZCWloacnBycPHkSP/74I1auXAlDw8bnaFaOMWliYlLvNqampgCAioqKercJDg5GcHCw6nOnTp3w8ssva3oqbVZkZKRenIemfk+RYMXhG0jILlZbvm5af7zat+4r2K3hafu9EBFR26F3gXLq1KmYOnWq6vP58+fx3nvvYc2aNcjPz8fmzZsb3YeZWU0fuYYGQ5dKpQAAc3NzLSumtkgmV+BsUh5+uJCO4zdqP8k/K8CzTYVJIiKi1qR3gfJx/v7++Omnn+Dt7Y0tW7ZgyZIl6NKlS4NtlLezCwsLIYpinbe9lbe6ldtS+yeKImIyCnHgaiaOXMuGpKz2HxSeDhb46/ieGOXj1AoVEhERtU16HygBwM3NDf369cP58+cRGxvbaKDs3r07gJqrkFlZWXB3d6+1TUpKitq21D4pFCJu5pTgWNw9HIzJxB1J3UNLdTA1wscvdsfMAE+YGD0VgyMQERFp7KkIlABQXV2t9rMhnTt3houLC+7du4eoqChMmzat1jZRUVEAaq6AUvshiiKS75fhXIoE55Lz8HtKPvLruBKp5GJthon93RD0vDccO5g+wUqJiIjaj6ciUN65cwexsbEAgL59+za6vSAImDRpEjZv3ozt27fXCpTR0dFITEyEsbExXn311RapmXQnI78c0cl5OJcsQXSyBLkl0ga372BqhHG9XTGxvxv8vRxgaMDBgYiIiBqiF4Hy8uXLOHToEGbOnKkaH1Lp2LFjWLBgAaqrqzFu3Dh07fpw+rwNGzZgw4YNGDx4MHbv3q3WbuHChdi+fTt+/vlnrF+/Hp999hkEQUBaWhrmzJkDoGb+cBcXl5Y/QdJYbkkl4jOLEZdZhLisIly/W6Q2bmR9TAwNMPJZR0zq744XfJxgZtz4aABERERUQy8CZUlJCVauXImVK1fCxcUFnTp1QlVVFdLT01FYWAgAGDRoEP7v//5PrV1hYSHS0tLg6elZa59eXl7YunUrZs+ejUWLFmHjxo1wcnJCXFwcZDIZBgwYgPXr1z+J06M6iKKI7KLKB8GxGPEPAmROccNXH5UMDQT06WSDgK4OGOLdEQO62MHchCGSiIioOfQiUPbt2xcbN27Er7/+ivj4eCQmJqKqqgoODg4YMmQIpk2bhrfffhtGRk073cDAQHTr1g1r165FdHQ0bty4AW9vb8yYMQOLFy9WDS9ELatUWo2U+6VIvl+KWzmliH8QIOt6Crs+ggD0crPGEG8HBHTtiEFe9rAy1Yv/+xMREbU6vfgvqp2dHT7++GN8/PHHTWoXEhKCkJCQBrcJCAjA4cOHtaiONCGKInKKpUh+EByTc0uRfL8Mt3NLca+48VvWj7O3NIGvuw183azR18MW/l72sLWof6B6IiIiaj69CJTUfkir5UiXlON27oPgeL9MFSDLquTN2qeztSl6u9ugl5tNTYh0t4aLtVm902YSERGRbjFQkk4pFCJySiqRLilHRkEFMvLLa14F5cjIr2jW1UYlAwHwsLdADxdr+Lpbo5e7DXq5WcOpA7seEBERtSYGSmoSURRRJhNx/W4R0lVBsRzp+eW4W1CBzIIKVMkVWh3DwsQQXR2t0NXRsuankxW6Olqhi4MFn74mIiJqgxgoSU21XIGcEimyCyuQXVSJ7KIKZBXW/MzIr7niWCKVA2fPan0sZ2vTB8HxQXh8EBxdbXi7moiIqD1hoHyKyBUi7pdIkVVUgewHIfHR0HivqBK5JZVQiLo5npGBADdbc3jYm6OzvQU62VnAw94Cne0t0NXREh3MjHVzICIiImpVDJR6QiZXIK9UipxiKe4pA2JxJbKUVxoLK5BTIoVcV2nxAccOpvCwM1cFRQ87C3R6ECBdrM1gZMh5r4mIiPQdA2UbJ1eIkJRJkVssRU5xJXIe/Mwtefg+p1gKSZkUom6zIoCawOhmYwYXGzN0sqsJjfdTEzDxpefRyc6Cg4ETERERA2VrEUURBeWyB4Gw8mFgfBAUcx8Exfulur+qqORgaQJXWzO42pjD1abmp9sjn52tzWBiVPsKY2TJTXR37tAiNREREVH7w0D5BBWU/3979x5UVb33cfyzkb03aALqoIiAiJpmPtno8RIerWOjjeZoZpp1egzFCbVnLJ3QLmaMOjWJw9EZk2dkNJvG6aSPl7R7XmZKRIaBslJRyMAbmmIYitz38wdn79xySVgbNizer5k9ym/9fr/9/eHAfFy/vdaq0BPvpepKcZmuFJcZvhq6IV06WhUS6K/QQL86Q2OPAD+umAYAAB5BoGxBZRVV+uFckaE5Oto6KCTAT90D7OoRUBMMu3f+8+89Auzq3tmPrWgAANBiCJSthN3X589AGOCnHp1r/t7jjvDI86cBAEBrQzppQX7WDoqJjlSw64zif/7s7KcAf1/uvQgAANokAmULCupoVcKU+71dBgAAgEdxk0AAAAAYQqAEAACAIQRKAAAAGEKgBAAAgCEESgBoZg6HQ4cPH1Z8fLxGjRqloKAg2Ww2hYaGavr06Tp06FCD49PS0jR16lQFBwfL399fgwYN0qpVq1RaWtrguAsXLuiFF15QeHi47Ha7IiIiFBcXpwsXLnhyeQBAoASA5nbw4EGNGTNGa9euVUZGhnr06KHBgweruLhYu3bt0rhx4/Tmm2/WOXbbtm0aM2aM9u7dK7vdrvvuu0+5ublasWKFxo4dq5KSkjrHnThxQg888IBSUlJUXFyswYMH648//tCmTZs0ZMgQZWdnN+eSAbQzBEoAaGYOh0P9+vXTxo0bdfXqVZ06dUpZWVkqLCzUa6+9JklavXq1Pv30U7dxeXl5io2NVVVVldasWaNz584pKytLOTk5GjBggDIyMrR06dJa71dVVaUZM2bo2rVrmj59ui5evKjMzExduHBBTz75pAoLC/X000+rurr5Hv8KoH0hUAJAMxsxYoROnjypBQsWqEuXLq52m82mt99+WxMnTpQkpaSkuI1LTExUWVmZJkyYoPj4eNfDD3r37q0tW7ZIkjZt2qTLly+7jdu1a5dOnDihbt266f3331fHjh0lSZ06ddLWrVvVrVs3/fjjj/rkk0+abc0A2hcCJQA0s4CAAPn61v8cifHjx0uSTp8+7WpzOBzavXu3JCk2NrbWmOjoaA0cOFAVFRW1guGuXbskSTNnzlTnzp3djnXu3FkzZsyQJO3YsaMJqwGA2giUAOBlzotr/P39XW1nz55VQUGBJGn06NF1jnO2p6enu7UfPXq0SeMAoKkIlADgRQ6Hw3Wm8PYAmJOTI0my2+0KDQ2tc2xUVJRbX0kqLy/X2bNn3Y7XNy4vL08VFRUGVwAABEoA8KqUlBR9//33stlsevnll13tv//+uyQpKCjI9dnJOzk/j+nsK0nXr193XWxz++c16xpXXV2tP/74o84+SUlJCgsLc71u3LjRyJUBaE8IlADgJVlZWXrppZck1Vzl3bdvX9cx5za4zWard7zdbpck3bp1q9a4hsY6x9059nZLlizR+fPnXa977rnnr5YDoB0jUAKAF/z666+aPHmySktL9eyzz+qVV15xO+7n5yepZgu7PmVlZZLcP3vpHNfQWOe4O8cCQFMRKAGghV26dEnjx49XQUGBHn/8cW3durXWtrZzW7qoqEgOh6POeZxb3bdvbQcGBsrHx8fteH3jfHx8FBAQYGwxACACJQC0qGvXrmn8+PH65Zdf9PDDD2vHjh2yWq21+vXv319SzdnEixcv1jnXmTNn3PpKNdvcERERbsfrGxcZGVnnewNAYxEoAaCF3LhxQ5MmTdLPP/+s4cOHa9++ffVuOUdERCgkJESSlJqaWmcfZ/vIkSPd2p1fN3YcADQVgbIFFZdWersEAF5SVlamqVOnKj09Xffff7++/PLLWjcdv53FYtG0adMkSZs3b651/MiRI8rOzpbVatWUKVPcjj355JOSpO3bt6u4uNjtWHFxses2RU899ZShNQGAE4GyBd0sq9T/ZZ73dhkAWlhVVZVmzZqlgwcPqm/fvvrmm2/UtWvXvxwXHx8vm82mr7/+WomJia7PUubn52vu3LmSpHnz5rnOZDpNnz5dAwcOVGFhoebMmaOSkhJJ0s2bNzVnzhwVFhZq8ODBeuKJJzy8UgDtVf3PAkOzWL7nJ/1Xr0ANCKn/zAQAc9m+fbv27NkjqeZCGOejD+/Us2dPt8ch9unTRykpKZozZ46WLl2q9evXq3v37vr5559VUVGhYcOGKTExsdY8HTp00I4dOzR27Fjt3LlT+/fvV79+/ZSbm6vr16+ra9eu+vjjj10X7wCAUQTKFlZaUa1/fXNa//vfw7xdCoAWcvttenJyctyebHO73r1712qbPXu2+vXrp3feeUdHjhzRiRMnFBUVpWeeeUbLli1zu03Q7QYPHqxjx45p5cqV+uKLL/TTTz8pODhYM2fO1IoVKxQWFuaZxQGACJReUc9DLwCYVExMjGJiYpo8Pjo6Wvv27Wv0uPDwcKWkpDT5fQHgbrHf0cJ8fSz6n3H9vF0GAACAxxAoW9iL/+in+0MDvV0GAACAxxAoW5Dd2kEv/oOzkwAAwFwIlC2oS0erbL58ywEAgLmQbgAAAGAIgRIAAACGECgBAABgiGkC5Z49exQXF6dhw4apZ8+estlsCgoKUnR0tNavX6/y8vJGzZeQkCCLxdLgKzs7u5lWAwAA0HaY5sbma9euVWpqqux2u0JDQzVkyBAVFBQoLS1NaWlp+vDDD7V//34FBQU1at7w8HBFRETUeaxjx46eKB0AAKBNM02gnDdvnlavXq3Ro0fLarW62o8ePaoZM2YoMzNTb7zxht57771GzTt37lwlJCR4uFoAAADzMM2Wd0xMjB555BG3MClJo0aNUlJSkqSabXEAAAB4lmkCZUMGDhwoSSopKfFyJQAAAOZjmi3vhqSlpUmShg4d2uixhw4d0vHjx1VYWKiuXbtqxIgRmj17tkJCQjxdJgAAQJtk2kBZVVWlgoIC7d27V6+++qo6deqkd955p9HzfPvtt25f79y5UwkJCdq4caNiYmI8VC0AAEDbZbot73Xr1sliscjX11fh4eF68cUX9eijj+ro0aMaMWLEXc/Ts2dPvf7668rIyFBhYaFKSkqUmpqqiRMn6tatW5o7d6727dvXjCsBAABoG0x3hrJXr14aPXq0KioqlJ+fr8uXL+vQoUP66KOPtHLlSnXo0OGu5omLi6vVFh0drc8++0zTp0/X7t27tXjxYk2ePFkWi6XOOZKSklwXBElSUVGRvvrqq6YtrBUpLS01xTrMhn8XAIC3WBwOh8PbRTSn9PR0xcXF6dixY5o/f76Sk5MNz3n69GkNGDBAkvTDDz9oyJAhdzUuLCxM58+fN/z+3vbVV1/pscce83YZuAP/Lq2PWX7mJXOtxZT+/W/pmWfc2y5ckEJDvVMPTKExP/em2/K+08iRI/X555/Lbrdr06ZNys/PNzznvffeq65du0qScnNzDc8HAADQlpk+UEpSaGioHnzwQVVXV+vYsWMemdN5v8vKykqPzAcAANBWtYtAKf0Z/DwRAK9evarffvtNUs3pYAAAgPasXQTKvLw815nJu/28Y0OSkpLkcDgUGBio4cOHG54PAACgLTNFoMzMzNRbb72lM2fO1Dr25ZdfauLEiaqsrNSkSZPUt29f17F169YpMjJSs2bNchtz/PhxLVy4UMePH3drLy0t1dtvv613331XkrRs2TLZbLZmWBEAAEDbYYrbBhUXF2vlypVauXKlQkJCFBYWpvLycp09e1ZFRUWSpOHDh+uDDz5wG1dUVKT8/HxFRka6tVdUVCg5OVnJyckKDg5WRESEJOnkyZOuxzfGxsbq1Vdfbf7FAQAAtHKmCJRDhgzR+vXrdeDAAR0/flzZ2dkqLy9Xt27d9NBDD2nmzJl67rnn5Ot7d8uNjIzUqlWrdOTIEWVnZ+vUqVMqLy9X9+7dNWnSJM2bN4/bswAAAPyHKQJlly5dtGjRIi1atKhR4xISEpSQkFCrPSgoSMuXL/dQdQAAAOZmis9QAgAAwHsIlAAAADCEQAkAAABDCJQAAAAwhEAJAAAAQwiUAAAAMIRACQAAAEMIlAAAADCEQAkAAABDCJQAAAAwhEAJAAAAQwiUAAAAMIRACQAAAEMIlAAAADCEQAkAAABDCJQAAAAwhEAJAAAAQwiUAAAAMIRACQAAAEMIlAAAADCEQAkAAABDCJQAAAAwhEAJAAAAQwiUAAAAMIRACQAAAEMIlAAAmFF5uVRd7e0q0E74ersAAADQDPr0qfnTx0ey2SSr1f1VV1tra3e2deggWSze/X6iQQRKAADMrLpaKi2tebVlrSXcGmn3Me/GMIESAIC2LiTE2xU0v4qKmldbVt/Z4sYE0759pYULpfBwb6/GDYESAIC2Ljpa+vvfpcOHvV0JGuKps8UffyydOlUTMFsJAiUAAG2dzSbt3y99+610/nzNBTnOM3q3v+pqb0zfv5qDi4Baxq+/SseOSX/7m7crcSFQAgBgBna7NH68d2uorvZsgPVk2L3bvpWV3v0e3q2bN71dgRsCJQAA8Awfn5pga7d7u5KmczhqQmVrCrvXrkmZmd7+zjSIQAkAAOBksfx5AUxrkZnZqra362Le69cBAADQIgiUAAAAMIRACQAAAEMIlAAAADCEQAkAAABDCJQAAAAwhEAJAAAAQwiUAAAAMMTUgXL58uWyWCyyWCxavXp1k+ZIS0vT1KlTFRwcLH9/fw0aNEirVq1SqdEHuwMAAJiEaQPlyZMnlZiYaGiObdu2acyYMdq7d6/sdrvuu+8+5ebmasWKFRo7dqxKSko8VC0AAEDbZcpA6XA4FBcXJ6vVqnHjxjVpjry8PMXGxqqqqkpr1qzRuXPnlJWVpZycHA0YMEAZGRlaunSphysHAABoe0wZKDdv3qzvvvtOK1asUHh4eJPmSExMVFlZmSZMmKD4+HhZLBZJUu/evbVlyxZJ0qZNm3T58mWP1Q0AANAWmS5QXrlyRcuWLdOgQYO0ePHiJs3hcDi0e/duSVJsbGyt49HR0Ro4cKAqKir0ySefGKoXAACgrTNdoFy8eLGuXbumjRs3ymq1NmmOs2fPqqCgQJI0evToOvs429PT05tWKAAAgEmYKlAeOHBA27Zt03PPPaeHH364yfPk5ORIkux2u0JDQ+vsExUV5dYXAACgvTJNoCwtLdX8+fMVGBiotWvXGprr999/lyQFBQW5Pjt5py5durj1BQAAaK98vV2Ap6xevVq5ubnasGGDevToYWgu5z0mbTZbvX3sdrsk6datW/X2SUpKUlJSkuvrS5cuKSwszFBtANqOK1eueLsEj7ly5Qq/vwBv6tXL/et//rPZ37Ixv8NMESid95wcOnSoFixYYHg+Pz8/SVJ5eXm9fcrKyiRJ/v7+9fZZsmSJlixZYrgeAPA25+88AKiLKba8Fy5cqMrKSiUnJ8vHx/iSnNvZRUVFcjgcdfZxbnU7+wIAALRXpjhD+f3338tisWjKlCm1jl2/fl2S9O6772rDhg0KDw9XRkZGg/P1799fUs3/yC9evKhed55mlnTmzBm3vgAAAO2VKQKlJFVVVTV4k/EbN27oxo0bru3shkRERCgkJESXLl1SamqqZs6cWatPamqqJGnkyJFNLxoAAMAETLHl7dyaruv1/PPPS5JWrVolh8OhvLy8v5zPYrFo2rRpkmqeunOnI0eOKDs7W1artc6zogAAAO2JKQJlU61bt06RkZGaNWtWrWPx8fGy2Wz6+uuvlZiY6PosZX5+vubOnStJmjdvnkJCQlq0ZgAAgNamXQfKoqIi5efn69KlS7WO9enTRykpKfLx8dHSpUsVHh6uoUOHqn///jp16pSGDRumxMREL1QNAADQurTrQPlXZs+ere+++06TJ0/WrVu3dOLECUVFRSkhIUGHDx9Wp06dvF0iAACA11kc9d0XBwAAALgLnKEEAACAIQRKAAAAGEKgBAAAgCEESgAAABhCoAQAAIAhBEoAAAAYQqAEAACAIQRKAAAAGEKgBAAAgCEESgAAABhCoAQAAIAhBEoAAAAY8v/o7oar2dt9MwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x640 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "array([[-4.25000000e-05+0.00000000e+00j, -6.23384943e+01+2.59821115e+01j,\n",
       "        -4.51876496e-09+3.32401277e-09j],\n",
       "       [-4.24799953e-05+0.00000000e+00j, -6.31467121e+01-5.44049254e+00j,\n",
       "         2.27143711e-09-4.57878622e-09j],\n",
       "       [-4.24599906e-05+0.00000000e+00j, -6.46633855e+01+1.72094304e+01j,\n",
       "         3.14852507e-10+4.28075655e-09j],\n",
       "       ...,\n",
       "       [ 4.24599906e-05+0.00000000e+00j, -6.38241199e+01+1.09681440e+01j,\n",
       "         3.36493846e-09+3.32097819e-09j],\n",
       "       [ 4.24799953e-05+0.00000000e+00j, -6.26693894e+01-2.06365700e+01j,\n",
       "        -5.18699923e-09-1.50159441e-09j],\n",
       "       [ 4.25000000e-05+0.00000000e+00j, -6.21856651e+01+2.64744314e+00j,\n",
       "         5.61788121e-09-1.01725131e-09j]])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "testModel.plotResult=True\n",
    "testModel.plotTime=4 # in micro-seconds, this controls the time axis of the model plot\n",
    "plt.rcParams.update({'font.size': 20}) # This changes the fint size of the tick labels\n",
    "testModel.runSim()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.15"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
